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Gholami Shahbandi, S. & Magnusson, M. (2018). 2D map alignment with region decomposition. Autonomous Robots
Open this publication in new window or tab >>2D map alignment with region decomposition
2018 (English)In: Autonomous Robots, ISSN 0929-5593, E-ISSN 1573-7527Article in journal (Refereed) Epub ahead of print
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.

Place, publisher, year, edition, pages
Springer, 2018
Keywords
Mobile robots, Mapping, Map alignment, Decomposition, 2D, Sensor map, Robot map, Layout map, Emergency map, Region segmentation, Similarity transformation
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-71107 (URN)10.1007/s10514-018-9785-7 (DOI)
Projects
ILIAD
Funder
EU, Horizon 2020Knowledge Foundation
Available from: 2019-01-04 Created: 2019-01-04 Last updated: 2019-01-08Bibliographically approved
Fan, H., Lu, D., Kucner, T. P., Magnusson, M. & Lilienthal, A. (2018). 2D Spatial Keystone Transform for Sub-Pixel Motion Extraction from Noisy Occupancy Grid Map. In: Proceedings of 21st International Conference on Information Fusion (FUSION): . Paper presented at 21st International Conference on Information Fusion (FUSION), Cambridge, UK, July 10 - 13, 2018 (pp. 2400-2406).
Open this publication in new window or tab >>2D Spatial Keystone Transform for Sub-Pixel Motion Extraction from Noisy Occupancy Grid Map
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2018 (English)In: Proceedings of 21st International Conference on Information Fusion (FUSION), 2018, p. 2400-2406Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we propose a novel sub-pixel motionextraction method, called as Two Dimensional Spatial KeystoneTransform (2DS-KST), for the motion detection and estimationfrom successive noisy Occupancy Grid Maps (OGMs). It extendsthe KST in radar imaging or motion compensation to 2Dreal spatial case, based on multiple hypotheses about possibledirections of moving obstacles. Simulation results show that 2DSKSThas a good performance on the extraction of sub-pixelmotions in very noisy environment, especially for those slowlymoving obstacles.

Keywords
robotics, occupancy grid map, motion extraction, keystone transform, 2DS-KST, sub-pixel
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-71953 (URN)10.23919/ICIF.2018.8455274 (DOI)978-0-9964527-6-2 (ISBN)978-1-5386-4330-3 (ISBN)
Conference
21st International Conference on Information Fusion (FUSION), Cambridge, UK, July 10 - 13, 2018
Available from: 2019-01-30 Created: 2019-01-30 Last updated: 2019-02-01Bibliographically approved
Fan, H., Kucner, T. P., Magnusson, M., Li, T. & Lilienthal, A. (2018). A Dual PHD Filter for Effective Occupancy Filtering in a Highly Dynamic Environment. IEEE transactions on intelligent transportation systems (Print), 19(9), 2977-2993
Open this publication in new window or tab >>A Dual PHD Filter for Effective Occupancy Filtering in a Highly Dynamic Environment
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2018 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 19, no 9, p. 2977-2993Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
Keywords
Mobile robot, occupancy filtering, PHD filter, BOF, particle filter, random finite set
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-63981 (URN)10.1109/TITS.2017.2770152 (DOI)000444611400021 ()2-s2.0-85038368968 (Scopus ID)
Note

Funding Agencies:

EU Project SPENCER  600877 

Marie Sklodowska-Curie Individual Fellowship  709267 

National Twelfth Five-Year Plan for Science and Technology Support of China  2014BAK12B03 

Available from: 2018-01-09 Created: 2018-01-09 Last updated: 2018-09-28Bibliographically approved
Mielle, M., Magnusson, M. & Lilienthal, A. J. (2018). A method to segment maps from different modalities using free space layout MAORIS: map of ripples segmentation. In: : . Paper presented at IEEE International Conference on Robotics and Automation (ICRA 2018), Brisbane, Australia, May 21-25, 2018 (pp. 4993-4999). IEEE Computer Society
Open this publication in new window or tab >>A method to segment maps from different modalities using free space layout MAORIS: map of ripples segmentation
2018 (English)Conference paper, Published 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.

Place, publisher, year, edition, pages
IEEE Computer Society, 2018
Keywords
map segmentation, free space, layout
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-68421 (URN)000446394503114 ()
Conference
IEEE International Conference on Robotics and Automation (ICRA 2018), Brisbane, Australia, May 21-25, 2018
Funder
EU, Horizon 2020, ICT-23-2014 645101 SmokeBotKnowledge Foundation, 20140220
Available from: 2018-08-09 Created: 2018-08-09 Last updated: 2018-10-22Bibliographically approved
Amigoni, F., Yu, W., Andre, T., Holz, D., Magnusson, M., Matteucci, M., . . . Madhavan, R. (2018). A Standard for Map Data Representation: IEEE 1873-2015 Facilitates Interoperability Between Robots. IEEE robotics & automation magazine, 25(1), 65-76
Open this publication in new window or tab >>A Standard for Map Data Representation: IEEE 1873-2015 Facilitates Interoperability Between Robots
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2018 (English)In: IEEE robotics & automation magazine, ISSN 1070-9932, E-ISSN 1558-223X, Vol. 25, no 1, p. 65-76Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
Keywords
Standards, Service robots, XML, Data models, Interoperability, Measurement
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-64331 (URN)10.1109/MRA.2017.2746179 (DOI)000427426900012 ()2-s2.0-85040906777 (Scopus ID)
Available from: 2018-01-17 Created: 2018-01-17 Last updated: 2018-08-30Bibliographically approved
Swaminathan, C. S., Kucner, T. P., Magnusson, M., Palmieri, L. & Lilienthal, A. (2018). Down the CLiFF: Flow-Aware Trajectory Planning under Motion Pattern Uncertainty. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): . Paper presented at 31st IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, October 1-5, 2018 (pp. 7403-7409). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Down the CLiFF: Flow-Aware Trajectory Planning under Motion Pattern Uncertainty
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2018 (English)In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 7403-7409Conference paper, Published 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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
Series
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858, E-ISSN 2153-0866
Keywords
Trajectory, Robots, Planning, Cost function, Uncertainty, Veichle dynamics, Aerospace electronics
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-70143 (URN)10.1109/IROS.2018.8593905 (DOI)000458872706106 ()978-1-5386-8094-0 (ISBN)978-1-5386-8095-7 (ISBN)
Conference
31st IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, October 1-5, 2018
Projects
ILIAD
Funder
EU, Horizon 2020, 732737
Available from: 2018-11-12 Created: 2018-11-12 Last updated: 2019-03-14Bibliographically approved
Almqvist, H., Magnusson, M., Kucner, T. P. & Lilienthal, A. (2018). Learning to detect misaligned point clouds. Journal of Field Robotics, 35(5), 662-677
Open this publication in new window or tab >>Learning to detect misaligned point clouds
2018 (English)In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 35, no 5, p. 662-677Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
John Wiley & Sons, 2018
Keywords
perception, mapping, position estimation
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-62985 (URN)10.1002/rob.21768 (DOI)000437836900002 ()2-s2.0-85037622789 (Scopus ID)
Projects
ILIADALLO
Funder
EU, Horizon 2020, 732737Knowledge Foundation, 20110214
Available from: 2017-12-05 Created: 2017-12-05 Last updated: 2018-07-27Bibliographically approved
Gholami Shahbandi, S., Magnusson, M. & Iagnemma, K. (2018). Nonlinear Optimization of Multimodal Two-Dimensional Map Alignment With Application to Prior Knowledge Transfer. Paper presented at IEEE International Conference on Robotics and Automation (ICRA 2018), Brisbane, Australia, May 21-25, 2018. IEEE Robotics and Automation Letters, 3(3), 2040-2047
Open this publication in new window or tab >>Nonlinear Optimization of Multimodal Two-Dimensional Map Alignment With Application to Prior Knowledge Transfer
2018 (English)In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 3, no 3, p. 2040-2047Article in journal (Refereed) Published
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%).

Place, publisher, year, edition, pages
IEEE Press, 2018
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-66564 (URN)10.1109/LRA.2018.2806439 (DOI)
Conference
IEEE International Conference on Robotics and Automation (ICRA 2018), Brisbane, Australia, May 21-25, 2018
Available from: 2018-04-12 Created: 2018-04-12 Last updated: 2018-04-12Bibliographically approved
Kucner, T. P., Magnusson, M., Schaffernicht, E., Hernandez Bennetts, V. M. & Lilienthal, A. (2017). Enabling Flow Awareness for Mobile Robots in Partially Observable Environments. IEEE Robotics and Automation Letters, 2(2), 1093-1100
Open this publication in new window or tab >>Enabling Flow Awareness for Mobile Robots in Partially Observable Environments
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2017 (English)In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 2, no 2, p. 1093-1100Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
Keywords
Field robots; mapping; social human-robot interaction
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-55174 (URN)10.1109/LRA.2017.2660060 (DOI)000413736600094 ()
Projects
ILIAD
Funder
Knowledge Foundation, 20140220 20130196
Note

Funding Agencies:

EU project SPENCER  ICT-2011-600877 

H2020-ICT project SmokeBot  645101 

H2020-ICT project ILIAD  732737 

Available from: 2017-02-01 Created: 2017-02-01 Last updated: 2017-11-23Bibliographically approved
Andreasson, H., Adolfsson, D., Stoyanov, T., Magnusson, M. & Lilienthal, A. (2017). Incorporating Ego-motion Uncertainty Estimates in Range Data Registration. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): . Paper presented at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017), Vancouver, Canada, September 24–28, 2017 (pp. 1389-1395). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Incorporating Ego-motion Uncertainty Estimates in Range Data Registration
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2017 (English)In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1389-1395Conference paper, Published 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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
Series
Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems, ISSN 2153-0858, E-ISSN 2153-0866
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-62803 (URN)10.1109/IROS.2017.8202318 (DOI)000426978201108 ()2-s2.0-85041958720 (Scopus ID)978-1-5386-2682-5 (ISBN)978-1-5386-2683-2 (ISBN)
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017), Vancouver, Canada, September 24–28, 2017
Projects
Semantic RobotsILIAD
Funder
Knowledge FoundationEU, Horizon 2020, 732737
Available from: 2017-11-24 Created: 2017-11-24 Last updated: 2018-04-09Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-8658-2985

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