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Canelhas, D. R., Schaffernicht, E., Stoyanov, T., Lilienthal, A. & Davison, A. J. (2017). Compressed Voxel-Based Mapping Using Unsupervised Learning. Robotics, 6(3), Article ID 15.
Open this publication in new window or tab >>Compressed Voxel-Based Mapping Using Unsupervised Learning
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2017 (English)In: Robotics, E-ISSN 2218-6581, Vol. 6, no 3, article id 15Article in journal (Refereed) Published
Abstract [en]

In order to deal with the scaling problem of volumetric map representations, we propose spatially local methods for high-ratio compression of 3D maps, represented as truncated signed distance fields. We show that these compressed maps can be used as meaningful descriptors for selective decompression in scenarios relevant to robotic applications. As compression methods, we compare using PCA-derived low-dimensional bases to nonlinear auto-encoder networks. Selecting two application-oriented performance metrics, we evaluate the impact of different compression rates on reconstruction fidelity as well as to the task of map-aided ego-motion estimation. It is demonstrated that lossily reconstructed distance fields used as cost functions for ego-motion estimation can outperform the original maps in challenging scenarios from standard RGB-D (color plus depth) data sets due to the rejection of high-frequency noise content.

Place, publisher, year, edition, pages
Basel, Switzerland: MDPI AG, 2017
Keywords
3D mapping, TSDF, compression, dictionary learning, auto-encoder, denoising
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-64420 (URN)10.3390/robotics6030015 (DOI)000419218300002 ()2-s2.0-85030989493 (Scopus ID)
Note

Funding Agencies:

European Commission  FP7-ICT-270350 

H-ICT  732737 

Available from: 2018-01-19 Created: 2018-01-19 Last updated: 2018-01-19Bibliographically 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
Ahtiainen, J., Stoyanov, T. & Saarinen, J. (2017). Normal Distributions Transform Traversability Maps: LIDAR-Only Approach for Traversability Mapping in Outdoor Environments. Journal of Field Robotics, 34(3), 600-621
Open this publication in new window or tab >>Normal Distributions Transform Traversability Maps: LIDAR-Only Approach for Traversability Mapping in Outdoor Environments
2017 (English)In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 34, no 3, p. 600-621Article in journal (Refereed) Published
Abstract [en]

Safe and reliable autonomous navigation in unstructured environments remains a challenge for field robots. In particular, operating on vegetated terrain is problematic, because simple purely geometric traversability analysis methods typically classify dense foliage as nontraversable. As traversing through vegetated terrain is often possible and even preferable in some cases (e.g., to avoid executing longer paths), more complex multimodal traversability analysis methods are necessary. In this article, we propose a three-dimensional (3D) traversability mapping algorithm for outdoor environments, able to classify sparsely vegetated areas as traversable, without compromising accuracy on other terrain types. The proposed normal distributions transform traversability mapping (NDT-TM) representation exploits 3D LIDAR sensor data to incrementally expand normal distributions transform occupancy (NDT-OM) maps. In addition to geometrical information, we propose to augment the NDT-OM representation with statistical data of the permeability and reflectivity of each cell. Using these additional features, we train a support-vector machine classifier to discriminate between traversable and nondrivable areas of the NDT-TM maps. We evaluate classifier performance on a set of challenging outdoor environments and note improvements over previous purely geometrical traversability analysis approaches.

Place, publisher, year, edition, pages
John Wiley & Sons, 2017
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-53368 (URN)10.1002/rob.21657 (DOI)000400272700008 ()2-s2.0-84971413791 (Scopus ID)
Note

Funding Agencies:

Finnish Society of Automation  

Finnish Funding Agency for Technology and Innovation (TEKES)  

Forum for Intelligent Machines (FIMA)  

Energy and Life Cycle Cost Efficient Machines (EFFIMA) research program 

Available from: 2016-11-02 Created: 2016-11-02 Last updated: 2018-01-13Bibliographically approved
Canelhas, D. R., Stoyanov, T. & Lilienthal, A. J. (2016). From Feature Detection in Truncated Signed Distance Fields to Sparse Stable Scene Graphs. IEEE Robotics and Automation Letters, 1(2), 1148-1155
Open this publication in new window or tab >>From Feature Detection in Truncated Signed Distance Fields to Sparse Stable Scene Graphs
2016 (English)In: IEEE Robotics and Automation Letters, ISSN 2377-3766, Vol. 1, no 2, p. 1148-1155Article in journal (Refereed) Published
Abstract [en]

With the increased availability of GPUs and multicore CPUs, volumetric map representations are an increasingly viable option for robotic applications. A particularly important representation is the truncated signed distance field (TSDF) that is at the core of recent advances in dense 3D mapping. However, there is relatively little literature exploring the characteristics of 3D feature detection in volumetric representations. In this paper we evaluate the performance of features extracted directly from a 3D TSDF representation. We compare the repeatability of Integral invariant features, specifically designed for volumetric images, to the 3D extensions of Harris and Shi & Tomasi corners. We also study the impact of different methods for obtaining gradients for their computation. We motivate our study with an example application for building sparse stable scene graphs, and present an efficient GPU-parallel algorithm to obtain the graphs, made possible by the combination of TSDF and 3D feature points. Our findings show that while the 3D extensions of 2D corner-detection perform as expected, integral invariants have shortcomings when applied to discrete TSDFs. We conclude with a discussion of the cause for these points of failure that sheds light on possible mitigation strategies.

Place, publisher, year, edition, pages
Piscataway, USA: Institute of Electrical and Electronics Engineers (IEEE), 2016
Keywords
Mapping, recognition
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-53369 (URN)10.1109/LRA.2016.2523555 (DOI)000413726900073 ()2-s2.0-84992291892 (Scopus ID)
Available from: 2016-11-02 Created: 2016-11-02 Last updated: 2018-03-09Bibliographically approved
Stoyanov, T., Krug, R., Muthusamy, R. & Kyrki, V. (2016). Grasp Envelopes: Extracting Constraints on Gripper Postures from Online Reconstructed 3D Models. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): . Paper presented at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016), Daejeong, Korea, October 9-14, 2016 (pp. 885-892). New York: Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Grasp Envelopes: Extracting Constraints on Gripper Postures from Online Reconstructed 3D Models
2016 (English)In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), New York: Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 885-892Conference paper, Published paper (Refereed)
Abstract [en]

Grasping systems that build upon meticulously planned hand postures rely on precise knowledge of object geometry, mass and frictional properties - assumptions which are often violated in practice. In this work, we propose an alternative solution to the problem of grasp acquisition in simple autonomous pick and place scenarios, by utilizing the concept of grasp envelopes: sets of constraints on gripper postures. We propose a fast method for extracting grasp envelopes for objects that fit within a known shape category, placed in an unknown environment. Our approach is based on grasp envelope primitives, which encode knowledge of human grasping strategies. We use environment models, reconstructed from noisy sensor observations, to refine the grasp envelope primitives and extract bounded envelopes of collision-free gripper postures. Also, we evaluate the envelope extraction procedure both in a stand alone fashion, as well as an integrated component of an autonomous picking system.

Place, publisher, year, edition, pages
New York: Institute of Electrical and Electronics Engineers (IEEE), 2016
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-53372 (URN)10.1109/IROS.2016.7759155 (DOI)000391921701009 ()978-1-5090-3762-9 (ISBN)
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016), Daejeong, Korea, October 9-14, 2016
Available from: 2016-11-02 Created: 2016-11-02 Last updated: 2018-01-13Bibliographically approved
Stoyanov, T., Vaskevicius, N., Mueller, C. A., Fromm, T., Krug, R., Tincani, V., . . . Echelmeyer, W. (2016). No More Heavy Lifting: Robotic Solutions to the Container-Unloading Problem. IEEE robotics & automation magazine, 23(4), 94-106
Open this publication in new window or tab >>No More Heavy Lifting: Robotic Solutions to the Container-Unloading Problem
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2016 (English)In: IEEE robotics & automation magazine, ISSN 1070-9932, E-ISSN 1558-223X, Vol. 23, no 4, p. 94-106Article in journal (Refereed) Published
Place, publisher, year, edition, pages
IEEE, 2016
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-53371 (URN)10.1109/MRA.2016.2535098 (DOI)000389874400011 ()2-s2.0-84981763797 (Scopus ID)
Note

Funding Agency:

EU FP7 project ROBLOG ICT-270350

Available from: 2016-11-02 Created: 2016-11-02 Last updated: 2018-01-13Bibliographically approved
Krug, R., Stoyanov, T., Tincani, V., Andreasson, H., Mosberger, R., Fantoni, G. & Lilienthal, A. J. (2016). The Next Step in Robot Commissioning: Autonomous Picking and Palletizing. IEEE Robotics and Automation Letters, 1(1), 546-553
Open this publication in new window or tab >>The Next Step in Robot Commissioning: Autonomous Picking and Palletizing
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2016 (English)In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 1, no 1, p. 546-553Article in journal (Refereed) Published
Abstract [en]

So far, autonomous order picking (commissioning) systems have not been able to meet the stringent demands regarding speed, safety, and accuracy of real-world warehouse automation, resulting in reliance on human workers. In this letter, we target the next step in autonomous robot commissioning: automatizing the currently manual order picking procedure. To this end, we investigate the use case of autonomous picking and palletizing with a dedicated research platform and discuss lessons learned during testing in simplified warehouse settings. The main theoretical contribution is a novel grasp representation scheme which allows for redundancy in the gripper pose placement. This redundancy is exploited by a local, prioritized kinematic controller which generates reactive manipulator motions on-the-fly. We validated our grasping approach by means of a large set of experiments, which yielded an average grasp acquisition time of 23.5 s at a success rate of 94.7%. Our system is able to autonomously carry out simple order picking tasks in a humansafe manner, and as such serves as an initial step toward future commercial-scale in-house logistics automation solutions.

Place, publisher, year, edition, pages
Piscataway, USA: Institute of Electrical and Electronics Engineers (IEEE), 2016
Keywords
Logistics, grasping, autonomous vehicle navigation, robot safety, mobile manipulation
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-53370 (URN)10.1109/LRA.2016.2519944 (DOI)000413719900073 ()2-s2.0-84981762372 (Scopus ID)
Funder
EU, FP7, Seventh Framework Programme, ICT-270350Knowledge Foundation, 20140220
Available from: 2016-11-02 Created: 2016-11-02 Last updated: 2018-01-13Bibliographically approved
Andreasson, H., Bouguerra, A., Cirillo, M., Dimitrov, D. N., Driankov, D., Karlsson, L., . . . Stoyanov, T. (2015). Autonomous transport vehicles: where we are and what is missing. IEEE robotics & automation magazine, 22(1), 64-75
Open this publication in new window or tab >>Autonomous transport vehicles: where we are and what is missing
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2015 (English)In: IEEE robotics & automation magazine, ISSN 1070-9932, E-ISSN 1558-223X, Vol. 22, no 1, p. 64-75Article in journal (Refereed) Published
Abstract [en]

In this article, we address the problem of realizing a complete efficient system for automated management of fleets of autonomous ground vehicles in industrial sites. We elicit from current industrial practice and the scientific state of the art the key challenges related to autonomous transport vehicles in industrial environments and relate them to enabling techniques in perception, task allocation, motion planning, coordination, collision prediction, and control. We propose a modular approach based on least commitment, which integrates all modules through a uniform constraint-based paradigm. We describe an instantiation of this system and present a summary of the results, showing evidence of increased flexibility at the control level to adapt to contingencies.

Keywords
Intelligent vehicles; Mobile robots; Resource management; Robot kinematics; Trajectory; Vehicle dynamics
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-44432 (URN)10.1109/MRA.2014.2381357 (DOI)000352030600010 ()2-s2.0-84925133099 (Scopus ID)
Available from: 2015-04-24 Created: 2015-04-24 Last updated: 2017-12-04Bibliographically approved
Magnusson, M., Vaskevicius, N., Stoyanov, T., Pathak, K. & Birk, A. (2015). Beyond points: Evaluating recent 3D scan-matching algorithms. In: 2015 IEEE International Conference on Robotics and Automation (ICRA): . Paper presented at 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, USA, May 26-30, 2015 (pp. 3631-3637). IEEE conference proceedings, 2015 June
Open this publication in new window or tab >>Beyond points: Evaluating recent 3D scan-matching algorithms
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2015 (English)In: 2015 IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings , 2015, Vol. 2015 June, p. 3631-3637Conference paper, Published 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.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2015
Series
Proceedings - IEEE International Conference on Robotics and Automation, ISSN 1050-4729 ; 2015-June
Keywords
Normal distribution, robot vision, 3D scan registration algorithm, 3D scan-matching algorithm, ICP method, MUMC algorithm, NDT, benchmark protocol, iterative closest point method, large-scale investigation, local surface structure, minimally uncertain maximum consensus algorithm, normal distribution transform, robot, Benchmark testing, Gaussian distribution, Iterative closest point algorithm, Optimization, Protocols, Three-dimensional displays, Transforms
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-45597 (URN)10.1109/ICRA.2015.7139703 (DOI)000370974903093 ()2-s2.0-84938280438 (Scopus ID)978-1-4799-6923-4 (ISBN)
Conference
2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, USA, May 26-30, 2015
Projects
ALLOSPENCERRobLog
Funder
Knowledge Foundation, 20110214EU, FP7, Seventh Framework Programme, ICT-2011-600877EU, FP7, Seventh Framework Programme, ICT-270350
Available from: 2015-08-19 Created: 2015-08-19 Last updated: 2018-01-11Bibliographically approved
Andreasson, H., Saarinen, J., Cirillo, M., Stoyanov, T. & Lilienthal, A. (2015). Fast, continuous state path smoothing to improve navigation accuracy. In: IEEE International Conference on Robotics and Automation (ICRA), 2015: . Paper presented at 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA, May 26-30, 2015 (pp. 662-669). IEEE Computer Society
Open this publication in new window or tab >>Fast, continuous state path smoothing to improve navigation accuracy
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2015 (English)In: IEEE International Conference on Robotics and Automation (ICRA), 2015, IEEE Computer Society, 2015, p. 662-669Conference paper, Published paper (Refereed)
Abstract [en]

Autonomous navigation in real-world industrial environments is a challenging task in many respects. One of the key open challenges is fast planning and execution of trajectories to reach arbitrary target positions and orientations with high accuracy and precision, while taking into account non-holonomic vehicle constraints. In recent years, lattice-based motion planners have been successfully used to generate kinematically and kinodynamically feasible motions for non-holonomic vehicles. However, the discretized nature of these algorithms induces discontinuities in both state and control space of the obtained trajectories, resulting in a mismatch between the achieved and the target end pose of the vehicle. As endpose accuracy is critical for the successful loading and unloading of cargo in typical industrial applications, automatically planned paths have not be widely adopted in commercial AGV systems. The main contribution of this paper addresses this shortcoming by introducing a path smoothing approach, which builds on the output of a lattice-based motion planner to generate smooth drivable trajectories for non-holonomic industrial vehicles. In real world tests presented in this paper we demonstrate that the proposed approach is fast enough for online use (it computes trajectories faster than they can be driven) and highly accurate. In 100 repetitions we achieve mean end-point pose errors below 0.01 meters in translation and 0.002 radians in orientation. Even the maximum errors are very small: only 0.02 meters in translation and 0.008 radians in orientation.

Place, publisher, year, edition, pages
IEEE Computer Society, 2015
Series
Proceedings - IEEE International Conference on Robotics and Automation, ISSN 1050-4729
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-47425 (URN)10.1109/ICRA.2015.7139250 (DOI)000370974900096 ()2-s2.0-84938229043 (Scopus ID)9781479969234 (ISBN)
Conference
2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA, May 26-30, 2015
Funder
Knowledge Foundation
Available from: 2016-01-15 Created: 2016-01-15 Last updated: 2018-01-10Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-6013-4874

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