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  • 1.
    Almqvist, Håkan
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Kucner, Tomasz Piotr
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Learning to detect misaligned point clouds2018In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 35, no 5, p. 662-677Article in journal (Refereed)
    Abstract [en]

    Matching and merging overlapping point clouds is a common procedure in many applications, including mobile robotics, three-dimensional mapping, and object visualization. However, fully automatic point-cloud matching, without manual verification, is still not possible because no matching algorithms exist today that can provide any certain methods for detecting misaligned point clouds. In this article, we make a comparative evaluation of geometric consistency methods for classifying aligned and nonaligned point-cloud pairs. We also propose a method that combines the results of the evaluated methods to further improve the classification of the point clouds. We compare a range of methods on two data sets from different environments related to mobile robotics and mapping. The results show that methods based on a Normal Distributions Transform representation of the point clouds perform best under the circumstances presented herein.

  • 2.
    Almqvist, Håkan
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Improving Point Cloud Accuracy Obtained from a Moving Platform for Consistent Pile Attack Pose Estimation2014In: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 75, no 1, p. 101-128Article in journal (Refereed)
    Abstract [en]

    We present a perception system for enabling automated loading with waist-articulated wheel loaders. To enable autonomous loading of piled materials, using either above-ground wheel loaders or underground load-haul-dump vehicles, 3D data of the pile shape is needed. However, using common 3D scanners, the scan data is distorted while the wheel loader is moving towards the pile. Existing methods that make use of 3D scan data (for autonomous loading as well as tasks such as mapping, localisation, and object detection) typically assume that each 3D scan is accurate. For autonomous robots moving over rough terrain, it is often the case that the vehicle moves a substantial amount during the acquisition of one 3D scan, in which case the scan data will be distorted. We present a study of auto-loading methods, and how to locate piles in real-world scenarios with nontrivial ground geometry. We have compared how consistently each method performs for live scans acquired in motion, and also how the methods perform with different view points and scan configurations. The system described in this paper uses a novel method for improving the quality of distorted 3D scans made from a vehicle moving over uneven terrain. The proposed method for improving scan quality is capable of increasing the accuracy of point clouds without assuming any specific features of the environment (such as planar walls), without resorting to a “stop-scan-go” approach, and without relying on specialised and expensive hardware. Each new 3D scan is registered to the preceding using the normal-distributions transform (NDT). After each registration, a mini-loop closure is performed with a local, per-scan, graph-based SLAM method. To verify the impact of the quality improvement, we present data that shows how auto-loading methods benefit from the corrected scans. The presented methods are validated on data from an autonomous wheel loader, as well as with simulated data. The proposed scan-correction method increases the accuracy of both the vehicle trajectory and the point cloud. We also show that it increases the reliability of pile-shape measures used to plan an efficient attack pose when performing autonomous loading.

  • 3.
    Almqvist, Håkan
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Improving Point-Cloud Accuracy from a Moving Platform in Field Operations2013In: 2013 IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings, 2013, p. 733-738Conference paper (Refereed)
    Abstract [en]

    This paper presents a method for improving the quality of distorted 3D point clouds made from a vehicle equipped with a laser scanner moving over uneven terrain. Existing methods that use 3D point-cloud data (for tasks such as mapping, localisation, and object detection) typically assume that each point cloud is accurate. For autonomous robots moving in rough terrain, it is often the case that the vehicle moves a substantial amount during the acquisition of one point cloud, in which case the data will be distorted. The method proposed in this paper is capable of increasing the accuracy of 3D point clouds, without assuming any specific features of the environment (such as planar walls), without resorting to a "stop-scan-go" approach, and without relying on specialised and expensive hardware. Each new point cloud is matched to the previous using normal-distribution-transform (NDT) registration, after which a mini-loop closure is performed with a local, per-scan, graph-based SLAM method. The proposed method increases the accuracy of both the measured platform trajectory and the point cloud. The method is validated on both real-world and simulated data.

  • 4.
    Amigoni, Francesco
    et al.
    Politecnico di Milano, Milan, Italy.
    Yu, Wonpil
    Electronics and Telecommunications Research Institute (ETRI), Daejeon, South Korea.
    Andre, Torsten
    University of Klagenfurt, Klagenfurt, Austria.
    Holz, Dirk
    University of Bonn, Bonn, Germany.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Matteucci, Matteo
    Politecnico di Milano, Milan, Italy.
    Moon, Hyungpil
    Sungkyunkwan University, Suwon, South Korea.
    Yokozuka, Masashi
    Nat. Inst. of Advanced Industrial Science and Technology, Tsukuba, Japan.
    Biggs, Geoffrey
    Nat. Inst. of Advanced Industrial Science and Technology, Tsukuba, Japan.
    Madhavan, Raj
    Amrita University, Clarksburg MD, United States of America.
    A Standard for Map Data Representation: IEEE 1873-2015 Facilitates Interoperability Between Robots2018In: IEEE robotics & automation magazine, ISSN 1070-9932, E-ISSN 1558-223X, Vol. 25, no 1, p. 65-76Article in journal (Refereed)
    Abstract [en]

    The availability of environment maps for autonomous robots enables them to complete several tasks. A new IEEE standard, IEEE 1873-2015, Robot Map Data Representation for Navigation (MDR) [15], sponsored by the IEEE Robotics and Automation Society (RAS) and approved by the IEEE Standards Association Standards Board in September 2015, defines a common representation for two-dimensional (2-D) robot maps and is intended to facilitate interoperability among navigating robots. The standard defines an extensible markup language (XML) data format for exchanging maps between different systems. This article illustrates how metric maps, topological maps, and their combinations can be represented according to the standard.

  • 5.
    Andreasson, Henrik
    et al.
    Örebro University, School of Science and Technology.
    Adolfsson, Daniel
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Incorporating Ego-motion Uncertainty Estimates in Range Data Registration2017In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1389-1395Conference paper (Refereed)
    Abstract [en]

    Local scan registration approaches commonlyonly utilize ego-motion estimates (e.g. odometry) as aninitial pose guess in an iterative alignment procedure. Thispaper describes a new method to incorporate ego-motionestimates, including uncertainty, into the objective function of aregistration algorithm. The proposed approach is particularlysuited for feature-poor and self-similar environments,which typically present challenges to current state of theart registration algorithms. Experimental evaluation showssignificant improvements in accuracy when using data acquiredby Automatic Guided Vehicles (AGVs) in industrial productionand warehouse environments.

  • 6.
    Andreasson, Henrik
    et al.
    Örebro University, Department of Technology.
    Magnusson, Martin
    Örebro University, Department of Technology.
    Lilienthal, Achim
    Örebro University, Department of Natural Sciences.
    Has something changed here?: Autonomous difference detection for security patrol robots2007In: 2007 IEEE/RSJ international conference on intelligent robots and systems, New York, NY, USA: IEEE, 2007, p. 3429-3435, article id 4399381Conference paper (Refereed)
    Abstract [en]

    This paper presents a system for autonomous change detection with a security patrol robot. In an initial step a reference model of the environment is created and changes are then detected with respect to the reference model as differences in coloured 3D point clouds, which are obtained from a 3D laser range scanner and a CCD camera. The suggested approach introduces several novel aspects, including a registration method that utilizes local visual features to determine point correspondences (thus essentially working without an initial pose estimate) and the 3D-NDT representation with adaptive cell size to efficiently represent both the spatial and colour aspects of the reference model. Apart from a detailed description of the individual parts of the difference detection system, a qualitative experimental evaluation in an indoor lab environment is presented, which demonstrates that the suggested system is able register and detect changes in spatial 3D data and also to detect changes that occur in colour space and are not observable using range values only.

  • 7.
    Fan, Hongqi
    et al.
    Örebro University, School of Science and Technology. National Laboratory of Science and Technology on Automatic Target Recognition, National University of Defense Technology, Changsha, China.
    Kucner, Tomasz Piotr
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Li, Tiancheng
    School of Sciences, University of Salamanca, Salamanca, Spain.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    A Dual PHD Filter for Effective Occupancy Filtering in a Highly Dynamic Environment2018In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 19, no 9, p. 2977-2993Article in journal (Refereed)
    Abstract [en]

    Environment monitoring remains a major challenge for mobile robots, especially in densely cluttered or highly populated dynamic environments, where uncertainties originated from environment and sensor significantly challenge the robot's perception. This paper proposes an effective occupancy filtering method called the dual probability hypothesis density (DPHD) filter, which models uncertain phenomena, such as births, deaths, occlusions, false alarms, and miss detections, by using random finite sets. The key insight of our method lies in the connection of the idea of dynamic occupancy with the concepts of the phase space density in gas kinetic and the PHD in multiple target tracking. By modeling the environment as a mixture of static and dynamic parts, the DPHD filter separates the dynamic part from the static one with a unified filtering process, but has a higher computational efficiency than existing Bayesian Occupancy Filters (BOFs). Moreover, an adaptive newborn function and a detection model considering occlusions are proposed to improve the filtering efficiency further. Finally, a hybrid particle implementation of the DPHD filter is proposed, which uses a box particle filter with constant discrete states and an ordinary particle filter with a time-varying number of particles in a continuous state space to process the static part and the dynamic part, respectively. This filter has a linear complexity with respect to the number of grid cells occupied by dynamic obstacles. Real-world experiments on data collected by a lidar at a busy roundabout demonstrate that our approach can handle monitoring of a highly dynamic environment in real time.

  • 8.
    Gholami Shahbandi, Saeed
    et al.
    Center for Applied Intelligent Systems Research, Halmstad University, Halmstad, Sweden.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    2D map alignment with region decomposition2018In: Autonomous Robots, ISSN 0929-5593, E-ISSN 1573-7527Article in journal (Refereed)
    Abstract [en]

    In many applications of autonomous mobile robots the following problem is encountered. Two maps of the same environment are available, one a prior map and the other a sensor map built by the robot. To benefit from all available information in both maps, the robot must find the correct alignment between the two maps. There exist many approaches to address this challenge, however, most of the previous methods rely on assumptions such as similar modalities of the maps, same scale, or existence of an initial guess for the alignment. In this work we propose a decomposition-based method for 2D spatial map alignment which does not rely on those assumptions. Our proposed method is validated and compared with other approaches, including generic data association approaches and map alignment algorithms. Real world examples of four different environments with thirty six sensor maps and four layout maps are used for this analysis. The maps, along with an implementation of the method, are made publicly available online.

  • 9.
    Gholami Shahbandi, Saeed
    et al.
    Center for Applied Intelligent Systems Research, Halmstad University, Halmstad, Sweden.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Iagnemma, Karl
    Robotic Mobility Group, Massachusetts Institute of Technology, Cambridge MA, USA.
    Nonlinear Optimization of Multimodal Two-Dimensional Map Alignment With Application to Prior Knowledge Transfer2018In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 3, no 3, p. 2040-2047Article in journal (Refereed)
    Abstract [en]

    We propose a method based on a nonlinear transformation for nonrigid alignment of maps of different modalities, exemplified with matching partial and deformed two-dimensional maps to layout maps. For two types of indoor environments, over a dataset of 40 maps, we have compared the method to state-of-the-art map matching and nonrigid image registration methods and demonstrate a success rate of 80.41% and a mean point-to-point alignment error of 1.78 m, compared to 31.9% and 10.7 m for the best alternative method. We also propose a fitness measure that can quite reliably detect bad alignments. Finally, we show a use case of transferring prior knowledge (labels/segmentation), demonstrating that map segmentation is more consistent when transferred from an aligned layout map than when operating directly on partial maps (95.97% vs. 81.56%).

  • 10.
    Huhle, Benjamin
    et al.
    Department of Graphical Interactive Systems WSI/GRIS, University of Tübingen, Germany.
    Magnusson, Martin
    Örebro University, Department of Technology.
    Straßer, Wolfgang
    Department of Graphical Interactive Systems WSI/GRIS, University of Tübingen, Germany.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Registration of colored 3D point clouds with a Kernel-based extension to the normal distributions transform2008In: 2008 IEEE international conference on robotics and automation, New York, NY, USA: IEEE, 2008, p. 4025-4030, article id 4543829Conference paper (Refereed)
    Abstract [en]

    We present a new algorithm for scan registration of colored 3D point data which is an extension to the Normal Distributions Transform (NDT). The probabilistic approach of NDT is extended to a color-aware registration algorithm by modeling the point distributions as Gaussian mixture-models in color space. We discuss different point cloud registration techniques, as well as alternative variants of the proposed algorithm. Results showing improved robustness of the proposed method using real-world data acquired with a mobile robot and a time-of-flight camera are presented.

  • 11.
    Kucner, Tomasz
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Tell me about dynamics!: Mapping velocity fields from sparse samples with Semi-Wrapped Gaussian Mixture Models2016In: Robotics: Science and Systems Conference (RSS 2016), 2016Conference paper (Refereed)
    Abstract [en]

    Autonomous mobile robots often require informa-tion about the environment beyond merely the shape of thework-space. In this work we present a probabilistic method formappingdynamics, in the sense of learning and representingstatistics about the flow of discrete objects (e.g., vehicles, people)as well as continuous media (e.g., air flow). We also demonstratethe capabilities of the proposed method with two use cases. Onerelates to motion planning in populated environments, whereinformation about the flow of people can help robots to followsocial norms and to learn implicit traffic rules by observingthe movements of other agents. The second use case relates toMobile Robot Olfaction (MRO), where information about windflow is crucial for most tasks, including e.g. gas detection, gasdistribution mapping and gas source localisation. We representthe underlying velocity field as a set of Semi-Wrapped GaussianMixture Models (SWGMM) representing the learnt local PDF ofvelocities. To estimate the parameters of the PDF we employ aformulation of Expectation Maximisation (EM) algorithm specificfor SWGMM. We also describe a data augmentation methodwhich allows to build a dense dynamic map based on a sparseset of measurements. In case only a small set of observations isavailable we employ a hierarchical sampling method to generatevirtual observations from existing mixtures.

  • 12.
    Kucner, Tomasz Piotr
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Where am I?: An NDT-based prior for MCL2015In: 2015 European Conference on Mobile Robots (ECMR), New York: IEEE conference proceedings , 2015Conference paper (Refereed)
    Abstract [en]

    One of the key requirements of autonomous mobile robots is a robust and accurate localisation system. Recent advances in the development of Monte Carlo Localisation (MCL) algorithms, especially the Normal Distribution Transform Monte Carlo Localisation (NDT-MCL), provides memory-efficient reliable localisation with industry-grade precision. We propose an approach for building an informed prior for NDT-MCL (in fact for any MCL algorithm) using an initial observation of the environment and its map. Leveraging on the NDT map representation, we build a set of poses using partial observations. After that we construct a Gaussian Mixture Model (GMM) over it. Next we obtain scores for each distribution in GMM. In this way we obtain in an efficient way a prior for NDT-MCL. Our approach provides a more focused then uniform initial distribution, concentrated in states where the robot is more likely to be, by building a Gaussian mixture model over potential poses. We present evaluations and quantitative results using real-world data from an indoor environment. Our experiments show that, compared to a uniform prior, the proposed method significantly increases the number of successful initialisations of NDT-MCL and reduces the time until convergence, at a negligible initial cost for computing the prior.

  • 13.
    Kucner, Tomasz Piotr
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor Manuel
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Enabling Flow Awareness for Mobile Robots in Partially Observable Environments2017In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 2, no 2, p. 1093-1100Article in journal (Refereed)
    Abstract [en]

    Understanding the environment is a key requirement for any autonomous robot operation. There is extensive research on mapping geometric structure and perceiving objects. However, the environment is also defined by the movement patterns in it. Information about human motion patterns can, e.g., lead to safer and socially more acceptable robot trajectories. Airflow pattern information allow to plan energy efficient paths for flying robots and improve gas distribution mapping. However, modelling the motion of objects (e.g., people) and flow of continuous media (e.g., air) is a challenging task. We present a probabilistic approach for general flow mapping, which can readily handle both of these examples. Moreover, we present and compare two data imputation methods allowing to build dense maps from sparsely distributed measurements. The methods are evaluated using two different data sets: one with pedestrian data and one with wind measurements. Our results show that it is possible to accurately represent multimodal, turbulent flow using a set of Gaussian Mixture Models, and also to reconstruct a dense representation based on sparsely distributed locations.

  • 14.
    Kucner, Tomasz
    et al.
    Örebro University, School of Science and Technology.
    Sarinen, Jari
    Aalto university, Helsinki, Finland.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Conditional transition maps: learning motion patterns in dynamic environments2013In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, 2013, p. 1196-1201Conference paper (Refereed)
    Abstract [en]

    In this paper we introduce a method for learning motion patterns in dynamic environments. Representations of dynamic environments have recently received an increasing amount of attention in the research community. Understanding dynamic environments is seen as one of the key challenges in order to enable autonomous navigation in real-world scenarios. However, representing the temporal dimension is a challenge yet to be solved. In this paper we introduce a spatial representation, which encapsulates the statistical dynamic behavior observed in the environment. The proposed Conditional Transition Map (CTMap) is a grid-based representation that associates a probability distribution for an object exiting the cell, given its entry direction. The transition parameters are learned from a temporal signal of occupancy on cells by using a local-neighborhood cross-correlation method. In this paper, we introduce the CTMap, the learning approach and present a proof-of-concept method for estimating future paths of dynamic objects, called Conditional Probability Propagation Tree (CPPTree). The evaluation is done using a real-world data-set collected at a busy roundabout.

  • 15.
    Magnusson, Martin
    Örebro University, Department of Technology. AASS.
    3D Scan Matching for Mobile Robots with Application to Mine Mapping: Licentiate Thesis2006Licentiate thesis, monograph (Other academic)
    Abstract [en]

    This thesis is concerned with three-dimensional scan registration, in particular of underground mine tunnels. Registration of partial range scans is an essential part of building 3D maps, and autonomous creation of reliable maps is a first step towards autonomous mining vehicles. The thesis presents a survey of relevant sensor technology and discusses the advantages and disadvantages of different sensors for use in mine environments. A survey of the state of the art in scan registration algorithms is also presented, as well as a number of relevant applications. A new algorithm for registration of 3D data is presented, which is a generalisation of the normal distributions transform (NDT) for 2D data developed by Biber and Straßer. A detailed quantitative and qualitative comparison of the new algorithm with existing registration algorithms is shown. Results with actual mine data, some of which were collected with a new prototype 3D laser scanner, show that the presented algorithm is faster and in many cases more accurate, compared with the current standard in 3D registration.

  • 16.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    The three-dimensional normal-distributions transform: an efficient representation for registration, surface analysis, and loop detection2009Doctoral thesis, monograph (Other academic)
    Abstract [en]

    This dissertation is concerned with three-dimensional (3D) sensing and 3D scan representation. Three-dimensional records are important tools in several disciplines; such as medical imaging, archaeology, and mobile robotics. This dissertation proposes the normal-distributions transform, NDT, as a general 3D surface representation with applications in scan registration, localisation, loop detection, and surface-structure analysis. After applying NDT, the surface is represented by a smooth function with analytic derivatives. This representation has several attractive properties.

    The smooth function representation makes it possible to use standard numerical optimisation methods, such as Newton’s method, for 3D registration. This dissertation extends the original two-dimensional NDT registration algorithm of Biber and Straßer to 3D and introduces a number of improvements. The 3D-NDT scan-registration algorithm is compared to current de facto standard registration algorithms. 3D-NDT scan registration with the proposed extensions is shown to be more robust, more accurate, and faster than the popular ICP algorithm. An additional benefit is that 3D-NDT registration provides a confidence measure of the result with little additional effort.

    Furthermore, a kernel-based extension to 3D-NDT for registering coloured data is proposed. Approaches based on local visual features typically use only a small fraction of the available 3D points for registration. In contrast, Colour-NDT uses all of the available 3D data. The dissertation proposes to use a combination of local visual features and Colour-NDT for robust registration of coloured 3D scans.

    Also building on NDT, a novel approach using 3D laser scans to perform appearance-based loop detection for mobile robots is proposed. Loop detection is an importantproblem in the SLAM (simultaneous localisation and mapping) domain. The proposed approach uses only the appearance of 3D point clouds to detect loops and requires nopose information. It exploits the NDT surface representation to create histograms based on local surface orientation and smoothness. The surface-shape histograms compress the input data by two to three orders of magnitude. Because of the high compression rate, the histograms can be matched efficiently to compare the appearance of two scans. Rotation invariance is achieved by aligning scans with respect to dominant surface orientations. In order to automatically determine the threshold that separates scans at loop closures from nonoverlapping ones, the proposed approach uses expectation maximisation to fit a Gamma mixture model to the output similarity measures.

    In order to enable more high-level tasks, it is desirable to extract semantic information from 3D models. One important task where such 3D surface analysis is useful is boulder detection for mining vehicles. This dissertation presents a method, also inspired by NDT, that provides clues as to where the pile is, where the bucket should be placed for loading, and where there are obstacles. The points of 3D point clouds are classified based on the surrounding surface roughness and orientation. Other potential applications include extraction of drivable paths over uneven surfaces.

  • 17.
    Magnusson, Martin
    et al.
    Örebro University, School of Science and Technology.
    Almqvist, Håkan
    Örebro University, School of Science and Technology.
    Consistent pile-shape quantification for autonomous wheel loaders2011In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011, p. 4078-4083Conference paper (Refereed)
    Abstract [en]

    This paper presents a study of approaches for selecting an efficient attack pose when loading piled materials with industrial construction vehicles. Automated handling of piled materials is a highly desired goal in many construction and mining applications. The main contributions of the paper are an experimental study of two novel approaches for selecting an attack pose from 3D data, compared to previously published approaches and extensions thereof. The outcome is based on quantitative validation, both with simulated data and data from a real-world scenario with nontrivial ground geometry.

  • 18.
    Magnusson, Martin
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Nüchter, A.
    Jacobs University Bremen, Bremen, Germany.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Appearance-based loop detection from 3D laser data using the normal distributions transform2009In: IEEE International Conference on Robotics and Automation 2009 (ICRA '09), IEEE conference proceedings, 2009, p. 23-28Conference paper (Other academic)
    Abstract [en]

    We propose a new approach to appearance based loop detection from metric 3D maps, exploiting the NDT surface representation. Locations are described with feature histograms based on surface orientation and smoothness, and loop closure can be detected by matching feature histograms. We also present a quantitative performance evaluation using two realworld data sets, showing that the proposed method works well in different environments.© 2009 IEEE.

  • 19.
    Magnusson, Martin
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Nüchter, Andreas
    Jacobs University Bremen.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Automatic appearance-based loop detection from three-dimensional laser data using the normal distributions transform2009In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 26, no 11-12, p. 892-914Article in journal (Refereed)
    Abstract [en]

    We propose a new approach to appearance-based loop detection for mobile robots, usingthree-dimensional (3D) laser scans. Loop detection is an important problem in the simultaneouslocalization and mapping (SLAM) domain, and, because it can be seen as theproblem of recognizing previously visited places, it is an example of the data associationproblem. Without a flat-floor assumption, two-dimensional laser-based approaches arebound to fail in many cases. Two of the problems with 3D approaches that we address inthis paper are how to handle the greatly increased amount of data and how to efficientlyobtain invariance to 3D rotations.We present a compact representation of 3D point cloudsthat is still discriminative enough to detect loop closures without false positives (i.e.,detecting loop closure where there is none). A low false-positive rate is very important becausewrong data association could have disastrous consequences in a SLAM algorithm.Our approach uses only the appearance of 3D point clouds to detect loops and requires nopose information. We exploit the normal distributions transform surface representationto create feature histograms based on surface orientation and smoothness. The surfaceshape histograms compress the input data by two to three orders of magnitude. Becauseof the high compression rate, the histograms can be matched efficiently to compare theappearance of two scans. Rotation invariance is achieved by aligning scans with respectto dominant surface orientations. We also propose to use expectation maximization to fit a gamma mixture model to the output similarity measures in order to automatically determinethe threshold that separates scans at loop closures from nonoverlapping ones.Wediscuss the problem of determining ground truth in the context of loop detection and thedifficulties in comparing the results of the few available methods based on range information.Furthermore, we present quantitative performance evaluations using three realworlddata sets, one of which is highly self-similar, showing that the proposed methodachieves high recall rates (percentage of correctly identified loop closures) at low falsepositiverates in environments with different characteristics.

  • 20.
    Magnusson, Martin
    et al.
    Örebro University, Department of Technology.
    Duckett, Tom
    Örebro University, Department of Technology.
    A comparison of 3D registration algorithms for autonomous underground mining vehicles2005Conference paper (Refereed)
    Abstract [en]

    The ICP algorithm and its derivatives is the de facto standard for registration of 3D range-finder scans today. This paper presents a quantitative comparison between ICP and 3D NDT, a novel approach based on the normal distributions transform. The new method ad- dresses two of the main problems of ICP: the fact that it does not make use of the local surface shape and the computationally demanding nearest-neighbour search. The results show that 3D NDT produces accurate results much faster, though it is more sensitive to error in the initial pose estimate.

  • 21.
    Magnusson, Martin
    et al.
    Örebro University, Department of Technology.
    Duckett, Tom
    Örebro University, Department of Technology.
    Elsrud, Rolf
    ̈Atlas Copco Rock Drills.
    Skagerlund, Lars-Erik
    Optab.
    3D modelling for underground mining vehicles2005Conference paper (Refereed)
    Abstract [en]

    This paper presents the basis of a new system for making detailed 3D models of underground tunnels. The system is to be used for automated control of mining vehicles. We describe some alternative methods for matching several partial scans, and their applicability for making a complete model of a mine environment

  • 22.
    Magnusson, Martin
    et al.
    Örebro University, School of Science and Technology.
    Kucner, Tomasz
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Quantitative Evaluation of Coarse-To-Fine Loading Strategies for Material Rehandling2015In: Proceedings of the IEEE International Conference on Automation Science and Engineering (CASE), New York: IEEE conference proceedings , 2015, p. 450-455Conference paper (Refereed)
    Abstract [en]

    Autonomous handling of piled materials is an emerging topic in automation science and engineering. A central question for material rehandling tasks (transporting materials that have been assembled in piles) is “where to dig, in order to optimise performance”? In particular, we are interested in the application of autonomous wheel loaders to handle piles of gravel. Still, the methodology proposed in this paper relates to granular materials in other applications too. Although initial work on suggesting strategies for where to dig has been done by a few other groups, there has been a lack of structured evaluation of the usefulness of the proposed strategies. In an attempt to further the field, we present a quantitative evaluation of loading strategies; both coarse ones, aiming to maintain a good pile shape over long-term operation; and refined ones, aiming to detect the locally best attack pose for acquiring a good fill grade in the bucket. Using real-world data from a semi-automated test platform, we present an assessment of how previously proposed pile shape measures can be mapped to the amount of material in the bucket after loading. We also present experimental data for long-term strategies, using simulations based on real-world 3D scan data from a production site.

  • 23.
    Magnusson, Martin
    et al.
    Örebro University, School of Science and Technology.
    Kucner, Tomasz Piotr
    Örebro University, School of Science and Technology.
    Gholami Shahbandi, Saeed
    IS lab, Halmstad University, Halmstad, Sweden.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Semi-Supervised 3D Place Categorisation by Descriptor Clustering2017In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 620-625Conference paper (Refereed)
    Abstract [en]

    Place categorisation; i. e., learning to group perception data into categories based on appearance; typically uses supervised learning and either visual or 2D range data.

    This paper shows place categorisation from 3D data without any training phase. We show that, by leveraging the NDT histogram descriptor to compactly encode 3D point cloud appearance, in combination with standard clustering techniques, it is possible to classify public indoor data sets with accuracy comparable to, and sometimes better than, previous supervised training methods. We also demonstrate the effectiveness of this approach to outdoor data, with an added benefit of being able to hierarchically categorise places into sub-categories based on a user-selected threshold.

    This technique relieves users of providing relevant training data, and only requires them to adjust the sensitivity to the number of place categories, and provide a semantic label to each category after the process is completed.

  • 24.
    Magnusson, Martin
    et al.
    Örebro University, Department of Technology.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Duckett, Tom
    Department of Computing and Informatics, University of Lincoln, Lincoln, United Kingdom.
    Scan registration for autonomous mining vehicles using 3D-NDT2007In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 24, no 10, p. 803-827Article in journal (Refereed)
    Abstract [en]

    Scan registration is an essential sub-task when building maps based on range finder data from mobile robots. The problem is to deduce how the robot has moved between consecutive scans, based on the shape of overlapping portions of the scans. This paper presents a new algorithm for registration of 3D data. The algorithm is a generalisation and improvement of the normal distributions transform (NDT) for 2D data developed by Biber and Straßer, which allows for accurate registration using a memory-efficient representation of the scan surface. A detailed quantitative and qualitative comparison of the new algorithm with the 3D version of the popular ICP (iterative closest point) algorithm is presented. Results with actual mine data, some of which were collected with a new prototype 3D laser scanner, show that the presented algorithm is faster and slightly more reliable than the standard ICP algorithm for 3D registration, while using a more memory-efficient scan surface representation.

  • 25.
    Magnusson, Martin
    et al.
    Örebro University, Department of Technology.
    Nüchter, Andreas
    Institute of Computer Science, University of Osnabrück, Osnabrück, Germany.
    Lörken, Christopher
    Institute of Computer Science, University of Osnabrück, Osnabrück, Germany.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Hertzberg, Joachim
    Institute of Computer Science, University of Osnabrück, Osnabrück, Germany.
    3D mapping the Kvarntorp mine: a rield experiment for evaluation of 3D scan matching algorithms2008In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Workshop, 2008Conference paper (Other academic)
    Abstract [en]

    This paper presents the results of a field experiment in the Kvarntorp mine outside of Örebro in Sweden. 3D mapping of the underground mine has been used to compare two scan matching methods, namely the iterative closest point algorithm (ICP) and the normal distributions transform (NDT). The experimental results of the algorithm are compared in terms of robustness and speed. For robustness we measure how reliably 3D scans are registered with respect to different starting pose estimates. Speed is evaluated running the authors’ best implementations on the same hardware. This leads to an unbiased comparison. In these experiments, NDT was shown to converge form a larger range of initial pose estimates than ICP, and to perform faster.

  • 26.
    Magnusson, Martin
    et al.
    Örebro University, School of Science and Technology.
    Nüchter, Andreas
    Jacobs University Bremen, Bremen, Germany; Knowledge Systems Research Group of the Institute of Computer Science, University of Osnabrück, Germany.
    Lörken, Christopher
    Institute of Computer Science, University of Osnabrück, Germany.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Hertzberg, Joachim
    Institute of Computer Science, University of Osnabrück, Germany.
    Evaluation of 3D registration reliability and speed: a comparison of ICP and NDT2009In: Proceedings of the 2009 IEEE international conference on Robotics and Automation, ICRA'09, IEEE conference proceedings, 2009, p. 2263-2268Conference paper (Refereed)
    Abstract [en]

    To advance robotic science it is important to perform experiments that can be replicated by other researchers to compare different methods. However, these comparisons tend to be biased, since re-implementations of reference methods often lack thoroughness and do not include the hands-on experience obtained during the original development process. This paper presents a thorough comparison of 3D scan registration algorithms based on a 3D mapping field experiment, carried out by two research groups that are leading in the field of 3D robotic mapping. The iterative closest points algorithm (ICP) is compared to the normal distributions transform (NDT). We also present an improved version of NDT with a substantially larger valley of convergence than previously published versions.

  • 27.
    Magnusson, Martin
    et al.
    Örebro University, School of Science and Technology.
    Vaskevicius, Narunas
    Deptartment of EECS, Jacobs University, Bremen, Germany.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Pathak, Kaustubh
    Deptartment of EECS, Jacobs University, Bremen, Germany.
    Birk, Andreas
    Deptartment of EECS, Jacobs University, Bremen, Germany.
    Beyond points: Evaluating recent 3D scan-matching algorithms2015In: 2015 IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings , 2015, Vol. 2015 June, p. 3631-3637Conference paper (Refereed)
    Abstract [en]

    Given that 3D scan matching is such a central part of the perception pipeline for robots, thorough and large-scale investigations of scan matching performance are still surprisingly few. A crucial part of the scientific method is to perform experiments that can be replicated by other researchers in order to compare different results. In light of this fact, this paper presents a thorough comparison of 3D scan registration algorithms using a recently published benchmark protocol which makes use of a publicly available challenging data set that covers a wide range of environments. In particular, we evaluate two types of recent 3D registration algorithms - one local and one global. Both approaches take local surface structure into account, rather than matching individual points. After well over 100 000 individual tests, we conclude that algorithms using the normal distributions transform (NDT) provides accurate results compared to a modern implementation of the iterative closest point (ICP) method, when faced with scan data that has little overlap and weak geometric structure. We also demonstrate that the minimally uncertain maximum consensus (MUMC) algorithm provides accurate results in structured environments without needing an initial guess, and that it provides useful measures to detect whether it has succeeded or not. We also propose two amendments to the experimental protocol, in order to provide more valuable results in future implementations.

  • 28.
    Mielle, Malcolm
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Using emergency maps to add not yet explored places into SLAM2017Conference paper (Other academic)
    Abstract [en]

    While using robots in search and rescue missions would help ensure the safety of first responders, a key issue is the time needed by the robot to operate. Even though SLAM is faster and faster, it might still be too slow to enable the use of robots in critical situations. One way to speed up operation time is to use prior information.

    We aim at integrating emergency-maps into SLAM to complete the SLAM map with information about not yet explored part of the environment. By integrating prior information, we can speed up exploration time or provide valuable prior information for navigation, for example, in case of sensor blackout/failure. However, while extensively used by firemen in their operations, emergency maps are not easy to integrate in SLAM since they are often not up to date or with non consistent scales.

    The main challenge we are tackling is in dealing with the imperfect scale of the rough emergency maps and integrate it with the online SLAM map in addition to challenges due to incorrect matches between these two types of map. We developed a formulation of graph-based SLAM incorporating information from an emergency map into SLAM, and propose a novel optimization process adapted to this formulation.

    We extract corners from the emergency map and the SLAM map, in between which we find correspondences using a distance measure. We then build a graph representation associating information from the emergency map and the SLAM map. Corners in the emergency map, corners in the robot map, and robot poses are added as nodes in the graph, while odometry, corner observations, walls in the emergency map, and corner associations are added as edges. To conserve the topology of the emergency map, but correct its possible errors in scale, edges representing the emergency map's walls are given a covariance so that they are easy to extend or shrink but hard to rotate. Correspondences between corners represent a zero transformation for the optimization to match them as close as possible. The graph optimization is done by using a combination robust kernels. We first use the Huber kernel, to converge toward a good solution, followed by Dynamic Covariance Scaling, to handle the remaining errors.

    We demonstrate our system in an office environment. We run the SLAM online during the exploration. Using the map enhanced by information from the emergency map, the robot was able to plan the shortest path toward a place it has not yet explored. This capability can be a real asset in complex buildings where exploration can take up a long time. It can also reduce exploration time by avoiding exploration of dead-ends, or search of specific places since the robot knows where it is in the emergency map.

  • 29.
    Mielle, Malcolm
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    SLAM auto-complete: completing a robot map using an emergency map2017In: 2017 IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR), IEEE conference proceedings, 2017, p. 35-40, article id 8088137Conference paper (Refereed)
    Abstract [en]

    In search and rescue missions, time is an important factor; fast navigation and quickly acquiring situation awareness might be matters of life and death. Hence, the use of robots in such scenarios has been restricted by the time needed to explore and build a map. One way to speed up exploration and mapping is to reason about unknown parts of the environment using prior information. While previous research on using external priors for robot mapping mainly focused on accurate maps or aerial images, such data are not always possible to get, especially indoor. We focus on emergency maps as priors for robot mapping since they are easy to get and already extensively used by firemen in rescue missions. However, those maps can be outdated, information might be missing, and the scales of rooms are typically not consistent.

    We have developed a formulation of graph-based SLAM that incorporates information from an emergency map. The graph-SLAM is optimized using a combination of robust kernels, fusing the emergency map and the robot map into one map, even when faced with scale inaccuracies and inexact start poses.

    We typically have more than 50% of wrong correspondences in the settings studied in this paper, and the method we propose correctly handles them. Experiments in an office environment show that we can handle up to 70% of wrong correspondences and still get the expected result. The robot can navigate and explore while taking into account places it has not yet seen. We demonstrate this in a test scenario and also show that the emergency map is enhanced by adding information not represented such as closed doors or new walls.

  • 30.
    Mielle, Malcolm
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    A method to segment maps from different modalities using free space layout MAORIS: map of ripples segmentation2018Conference paper (Refereed)
    Abstract [en]

    How to divide floor plans or navigation maps into semantic representations, such as rooms and corridors, is an important research question in fields such as human-robot interaction, place categorization, or semantic mapping. While most works focus on segmenting robot built maps, those are not the only types of map a robot, or its user, can use. We present a method for segmenting maps from different modalities, focusing on robot built maps and hand-drawn sketch maps, and show better results than state of the art for both types.

    Our method segments the map by doing a convolution between the distance image of the map and a circular kernel, and grouping pixels of the same value. Segmentation is done by detecting ripple-like patterns where pixel values vary quickly, and merging neighboring regions with similar values.

    We identify a flaw in the segmentation evaluation metric used in recent works and propose a metric based on Matthews correlation coefficient (MCC). We compare our results to ground-truth segmentations of maps from a publicly available dataset, on which we obtain a better MCC than the state of the art with 0.98 compared to 0.65 for a recent Voronoi-based segmentation method and 0.70 for the DuDe segmentation method.

    We also provide a dataset of sketches of an indoor environment, with two possible sets of ground truth segmentations, on which our method obtains an MCC of 0.56 against 0.28 for the Voronoi-based segmentation method and 0.30 for DuDe.

  • 31.
    Mielle, Malcolm
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Using sketch-maps for robot navigation: interpretation and matching2016In: 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), New York: Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 252-257Conference paper (Refereed)
    Abstract [en]

    We present a study on sketch-map interpretationand sketch to robot map matching, where maps have nonuniform scale, different shapes or can be incomplete. For humans, sketch-maps are an intuitive way to communicate navigation information, which makes it interesting to use sketch-maps forhuman robot interaction; e.g., in emergency scenarios.

    To interpret the sketch-map, we propose to use a Voronoi diagram that is obtained from the distance image on which a thinning parameter is used to remove spurious branches. The diagram is extracted as a graph and an efficient error-tolerant graph matching algorithm is used to find correspondences, while keeping time and memory complexity low.

    A comparison against common algorithms for graph extraction shows that our method leads to twice as many good matches. For simple maps, our method gives 95% good matches even for heavily distorted sketches, and for a more complex real-world map, up to 58%. This paper is a first step toward using unconstrained sketch-maps in robot navigation.

  • 32.
    Palmieri, Luigi
    et al.
    Computer Science Department, University of Freiburg, Freiburg im Breisgau, Germany.
    Kucner, Tomasz
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Arras, Kai
    Bosch Corporate Research, Stuttgart, Germany.
    Kinodynamic Motion Planning on Gaussian Mixture Fields2017In: IEEE International Conference on Robotics and Automation (ICRA 2017), 2017Conference paper (Refereed)
    Abstract [en]

    We present a mobile robot motion planning ap-proach under kinodynamic constraints that exploits learnedperception priors in the form of continuous Gaussian mixturefields. Our Gaussian mixture fields are statistical multi-modalmotion models of discrete objects or continuous media in theenvironment that encode e.g. the dynamics of air or pedestrianflows. We approach this task using a recently proposed circularlinear flow field map based on semi-wrapped GMMs whosemixture components guide sampling and rewiring in an RRT*algorithm using a steer function for non-holonomic mobilerobots. In our experiments with three alternative baselines,we show that this combination allows the planner to veryefficiently generate high-quality solutions in terms of pathsmoothness, path length as well as natural yet minimum controleffort motions through multi-modal representations of Gaussianmixture fields.

  • 33.
    Stoyanov, Todor
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Almqvist, Håkan
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    On the Accuracy of the 3D Normal Distributions Transform as a Tool for Spatial Representation2011In: 2011 IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings, 2011Conference paper (Refereed)
    Abstract [en]

    The Three-Dimensional Normal Distributions Transform (3D-NDT) is a spatial modeling technique with applications in point set registration, scan similarity comparison, change detection and path planning. This work concentrates on evaluating three common variations of the 3D-NDT in terms of accuracy of representing sampled semi-structured environments. In a novel approach to spatial representation quality measurement, the 3D geometrical modeling task is formulated as a classification problem and its accuracy is evaluated with standard machine learning performance metrics. In this manner the accuracy of the 3D-NDT variations is shown to be comparable to, and in some cases to outperform that of the standard occupancy grid mapping model.

  • 34.
    Stoyanov, Todor
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Path planning in 3D environments using the normal distributions transform2010In: IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems (IROS 2010), IEEE conference proceedings, 2010, p. 3263-3268Conference paper (Refereed)
    Abstract [en]

    Planning feasible paths in fully three-dimensional environments is a challenging problem. Application of existing algorithms typically requires the use of limited 3D representations that discard potentially useful information. This article proposes a novel approach to path planning that utilizes a full 3D representation directly: the Three-Dimensional Normal Distributions Transform (3D-NDT). The well known wavefront planner is modified to use 3D-NDT as a basis for map representation and evaluated using both indoor and outdoor data sets. The use of 3D-NDT for path planning is thus demonstrated to be a viable choice with good expressive capabilities.

  • 35.
    Stoyanov, Todor
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Comparative evaluation of the consistency of three-dimensional spatial representations used in autonomous robot navigation2013In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 30, no 2, p. 216-236Article in journal (Refereed)
    Abstract [en]

    An increasing number of robots for outdoor applications rely on complex three-dimensional (3D) environmental models. In many cases, 3D maps are used for vital tasks, such as path planning and collision detection in challenging semistructured environments. Thus, acquiring accurate three-dimensional maps is an important research topic of high priority for autonomously navigating robots. This article proposes an evaluation method that is designed to compare the consistency with which different representations model the environment. In particular, the article examines several popular (probabilistic) spatial representations that are capable of predicting the occupancy of any point in space, given prior 3D range measurements. This work proposes to reformulate the obtained environmental models as probabilistic binary classifiers, thus allowing for the use of standard evaluation and comparison procedures. To avoid introducing localization errors, this article concentrates on evaluating models constructed from measurements acquired at fixed sensor poses. Using a cross-validation approach, the consistency of different representations, i.e., the likelihood of correctly predicting unseen measurements in the sensor field of view, can be evaluated. Simulated and real-world data sets are used to benchmark the precision of four spatial models—occupancy grid, triangle mesh, and two variations of the three-dimensional normal distributions transform (3D-NDT)—over various environments and sensor noise levels. Overall, the consistency of representation of the 3D-NDT is found to be the highest among the tested models, with a similar performance over varying input data.

  • 36.
    Stoyanov, Todor
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Point Set Registration through Minimization of the L-2 Distance between 3D-NDT Models2012In: 2012 IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings, 2012, p. 5196-5201Conference paper (Refereed)
    Abstract [en]

    Point set registration — the task of finding the best fitting alignment between two sets of point samples, is an important problem in mobile robotics. This article proposes a novel registration algorithm, based on the distance between Three- Dimensional Normal Distributions Transforms. 3D-NDT models — a sub-class of Gaussian Mixture Models with uniformly weighted, largely disjoint components, can be quickly computed from range point data. The proposed algorithm constructs 3DNDT representations of the input point sets and then formulates an objective function based on the L2 distance between the considered models. Analytic first and second order derivatives of the objective function are computed and used in a standard Newton method optimization scheme, to obtain the best-fitting transformation. The proposed algorithm is evaluated and shown to be more accurate and faster, compared to a state of the art implementation of the Iterative Closest Point and 3D-NDT Point-to-Distribution algorithms.

  • 37.
    Stoyanov, Todor
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Fast and accurate scan registration through minimization of the distance between compact 3D NDT Representations2012In: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176, Vol. 31, no 12, p. 1377-1393Article in journal (Refereed)
    Abstract [en]

    Registration of range sensor measurements is an important task in mobile robotics and has received a lot of attention. Several iterative optimization schemes have been proposed in order to align three-dimensional (3D) point scans. With the more widespread use of high-frame-rate 3D sensors and increasingly more challenging application scenarios for mobile robots, there is a need for fast and accurate registration methods that current state-of-the-art algorithms cannot always meet. This work proposes a novel algorithm that achieves accurate point cloud registration an order of a magnitude faster than the current state of the art. The speedup is achieved through the use of a compact spatial representation: the Three-Dimensional Normal Distributions Transform (3D-NDT). In addition, a fast, global-descriptor based on the 3D-NDT is defined and used to achieve reliable initial poses for the iterative algorithm. Finally, a closed-form expression for the covariance of the proposed method is also derived. The proposed algorithms are evaluated on two standard point cloud data sets, resulting in stable performance on a par with or better than the state of the art. The implementation is available as an open-source package for the Robot Operating system (ROS).

  • 38.
    Swaminathan, Chittaranjan Srinivas
    et al.
    Örebro University, School of Science and Technology.
    Kucner, Tomasz Piotr
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Palmieri, Luigi
    Robert Bosch, GmbH Corporate Research, Germany.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Down the CLiFF: Flow-Aware Trajectory Planning under Motion Pattern Uncertainty2018In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 7403-7409Conference paper (Refereed)
    Abstract [en]

    In this paper we address the problem of flow-aware trajectory planning in dynamic environments considering flow model uncertainty. Flow-aware planning aims to plan trajectories that adhere to existing flow motion patterns in the environment, with the goal to make robots more efficient, less intrusive and safer. We use a statistical model called CLiFF-map that can map flow patterns for both continuous media and discrete objects. We propose novel cost and biasing functions for an RRT* planning algorithm, which exploits all the information available in the CLiFF-map model, including uncertainties due to flow variability or partial observability. Qualitatively, a benefit of our approach is that it can also be tuned to yield trajectories with different qualities such as exploratory or cautious, depending on application requirements. Quantitatively, we demonstrate that our approach produces more flow-compliant trajectories, compared to two baselines.

  • 39.
    Triebel, Rudolph
    et al.
    Department of Computer Science, Technische Universität München, Munich, Germany.
    Arras, Kai
    Social Robotics Lab, University of Freiburg, Freiburg im Breisgau, Germany.
    Alami, Rachid
    Laboratory for Analysis and Architecture of Systems (LAAS-CNRS), Toulouse, France.
    Beyer, Lucas
    Rheinisch-Westfälische Technische Hochschule, Aachen, Germany.
    Breuers, Stefan
    Rheinisch-Westfälische Technische Hochschule, Aachen, Germany.
    Chatila, Raja
    Institute for Intelligent Systems and Robotics (ISIR-CNRS), Paris, France.
    Chetouani, Mohamed
    Institute for Intelligent Systems and Robotics (ISIR-CNRS), Paris, France.
    Cremers, Daniel
    Department of Computer Science, Technische Universität München, Munich, Germany.
    Evers, Vanessa
    University of Twente, Enschede, Netherlands.
    Fiore, Michelangelo
    Laboratory for Analysis and Architecture of Systems (LAAS-CNRS), Toulouse, France.
    Hung, Hayley
    Delft University of Technology, Delft, Netherlands.
    Ramirez, Omar A. Islas
    Institute for Intelligent Systems and Robotics (ISIR-CNRS), Paris, France.
    Joosse, Michiel
    University of Twente, Enschede, Netherlands.
    Khambhaita, Harmish
    Laboratory for Analysis and Architecture of Systems (LAAS-CNRS), Toulouse, France.
    Kucner, Tomasz
    Örebro University, School of Science and Technology.
    Leibe, Bastian
    Rheinisch-Westfälische Technische Hochschule, Aachen, Germany.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Linder, Timm
    Social Robotics Lab, University of Freiburg, Freiburg im Breisgau, Germany.
    Lohse, Manja
    University of Twente, Enschede, Netherlands.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Okal, Billy
    Social Robotics Lab, University of Freiburg, Freiburg im Breisgau, Germany.
    Palmieri, Luigi
    Social Robotics Lab, University of Freiburg, Freiburg im Breisgau, Germany.
    Rafi, Umer
    Rheinisch-Westfälische Technische Hochschule, Aachen, Germany.
    van Rooij, Marieke
    University of Amsterdam, Amsterdam, Netherlands.
    Zhang, Lu
    University of Twente, Enschede, Netherlands; Delft University of Technology, Delft, Netherlands.
    SPENCER: A Socially Aware Service Robot for Passenger Guidance and Help in Busy Airports2016In: Field and Service Robotics: Results of the 10th International Conference / [ed] David S. Wettergreen, Timothy D. Barfoot, Springer, 2016, p. 607-622Conference paper (Refereed)
    Abstract [en]

    We present an ample description of a socially compliant mobile robotic platform, which is developed in the EU-funded project SPENCER. The purpose of this robot is to assist, inform and guide passengers in large and busy airports. One particular aim is to bring travellers of connecting flights conveniently and efficiently from their arrival gate to the passport control. The uniqueness of the project stems from the strong demand of service robots for this application with a large potential impact for the aviation industry on one side, and on the other side from the scientific advancements in social robotics, brought forward and achieved in SPENCER. The main contributions of SPENCER are novel methods to perceive, learn, and model human social behavior and to use this knowledge to plan appropriate actions in real-time for mobile platforms. In this paper, we describe how the project advances the fields of detection and tracking of individuals and groups, recognition of human social relations and activities, normative human behavior learning, socially-aware task and motion planning, learning socially annotated maps, and conducting empirical experiments to assess socio-psychological effects of normative robot behaviors.

  • 40.
    Zaganidis, Anestis
    et al.
    Lincoln Centre for Autonomous SysteLincoln Centre for Autonomous Systems (LCAS), University of Lincoln, Lincoln, UK.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Duckett, Tom
    Lincoln Centre for Autonomous Systems (LCAS), University of Lincoln, Lincoln, UK.
    Cielniak, Grzegorz
    Lincoln Centre for Autonomous Systems (LCAS), University of Lincoln, Lincoln, UK.
    Semantic-assisted 3D Normal Distributions Transform for scan registration in environments with limited structure2017In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE Robotics and Automation Society, 2017, p. 4064-4069Conference paper (Refereed)
    Abstract [en]

    Point cloud registration is a core problem of many robotic applications, including simultaneous localization and mapping. The Normal Distributions Transform (NDT) is a method that fits a number of Gaussian distributions to the data points, and then uses this transform as an approximation of the real data, registering a relatively small number of distributions as opposed to the full point cloud. This approach contributes to NDT’s registration robustness and speed but leaves room for improvement in environments of limited structure.

    To address this limitation we propose a method for the introduction of semantic information extracted from the point clouds into the registration process. The paper presents a large scale experimental evaluation of the algorithm against NDT on two publicly available benchmark data sets. For the purpose of this test a measure of smoothness is used for the semantic partitioning of the point clouds. The results indicate that the proposed method improves the accuracy, robustness and speed of NDT registration, especially in unstructured environments, making NDT suitable for a wider range of applications.

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