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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, May 21 - May 25, 2018.
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.

Keywords
map segmentation, free space, layout
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-68421 (URN)
Conference
IEEE International Conference on Robotics and Automation (ICRA 2018), Brisbane, May 21 - May 25, 2018
Funder
EU, Horizon 2020, ICT-23-2014 645101 SmokeBot
Available from: 2018-08-09 Created: 2018-08-09 Last updated: 2018-08-13Bibliographically 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: 2018-04-09Bibliographically 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
Fan, H., Kucner, T. P., Magnusson, M., Li, T. & Lilienthal, A. (2017). A Dual PHD Filter for Effective Occupancy Filtering in a Highly Dynamic Environment. IEEE transactions on intelligent transportation systems (Print), PP(99), 1-17
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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2017 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. PP, no 99, p. 1-17Article in journal (Refereed) Epub ahead of print
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), 2017
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)
Available from: 2018-01-09 Created: 2018-01-09 Last updated: 2018-08-13Bibliographically 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
Palmieri, L., Kucner, T., Magnusson, M., Lilienthal, A. & Arras, K. (2017). Kinodynamic Motion Planning on Gaussian Mixture Fields. In: IEEE International Conference on Robotics and Automation (ICRA 2017): . Paper presented at IEEE International Conference on Robotics and Automation (ICRA 2017), Singapore, May 29 - June 03, 2017.
Open this publication in new window or tab >>Kinodynamic Motion Planning on Gaussian Mixture Fields
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2017 (English)In: IEEE International Conference on Robotics and Automation (ICRA 2017), 2017Conference paper, Published 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.

National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-55177 (URN)
Conference
IEEE International Conference on Robotics and Automation (ICRA 2017), Singapore, May 29 - June 03, 2017
Available from: 2017-02-01 Created: 2017-02-01 Last updated: 2018-07-30Bibliographically approved
Magnusson, M., Kucner, T. P., Gholami Shahbandi, S., Andreasson, H. & Lilienthal, A. (2017). Semi-Supervised 3D Place Categorisation by Descriptor Clustering. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): . Paper presented at 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017) Vancouver, Canada, September 24–28, 2017 (pp. 620-625). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Semi-Supervised 3D Place Categorisation by Descriptor Clustering
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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. 620-625Conference paper, Published 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.

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-61903 (URN)10.1109/IROS.2017.8202216 (DOI)000426978201006 ()2-s2.0-85041949592 (Scopus ID)978-1-5386-2682-5 (ISBN)978-1-5386-2683-2 (ISBN)
Conference
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017) Vancouver, Canada, September 24–28, 2017
Projects
ILIAD
Funder
EU, Horizon 2020, 732737
Note

Iliad Project: http://iliad-project.eu

Available from: 2017-10-20 Created: 2017-10-20 Last updated: 2018-04-09Bibliographically approved
Mielle, M., Magnusson, M., Andreasson, H. & Lilienthal, A. J. (2017). SLAM auto-complete: completing a robot map using an emergency map. In: 2017 IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR): . Paper presented at 15th IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR 2017), ShanghaiTech University, China, October 11-13, 2017 (pp. 35-40). IEEE conference proceedings, Article ID 8088137.
Open this publication in new window or tab >>SLAM auto-complete: completing a robot map using an emergency map
2017 (English)In: 2017 IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR), IEEE conference proceedings, 2017, p. 35-40, article id 8088137Conference paper, Published 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.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2017
Keywords
SLAM, robotics, graph, graph SLAM, emergency map, rescue, exploration, auto complete, SLAM, robotics, graph, graph SLAM, plan de secours, sauvetage, exploration, auto complete
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-62057 (URN)10.1109/SSRR.2017.8088137 (DOI)000426991900007 ()2-s2.0-85040221684 (Scopus ID)978-1-5386-3923-8 (ISBN)978-1-5386-3924-5 (ISBN)
Conference
15th IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR 2017), ShanghaiTech University, China, October 11-13, 2017
Projects
EU H2020 project SmokeBot (ICT- 23-2014 645101)
Funder
Knowledge Foundation, 20140220
Note

Funding Agency:

EU  ICT-23-2014645101

Available from: 2017-11-08 Created: 2017-11-08 Last updated: 2018-03-27Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-8658-2985

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