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Detection and Tracking of General Movable Objects in Large Three-Dimensional Maps
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.ORCID iD: 0000-0003-1189-6634
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.ORCID iD: 0000-0002-1170-7162
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.ORCID iD: 0000-0002-7796-1438
2019 (English)In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 35, no 1, p. 231-247Article in journal (Refereed) Published
Abstract [en]

This paper studies the problem of detection and tracking of general objects with semistatic dynamics observed by a mobile robot moving in a large environment. A key problem is that due to the environment scale, the robot can only observe a subset of the objects at any given time. Since some time passes between observations of objects in different places, the objects might be moved when the robot is not there. We propose a model for this movement in which the objects typically only move locally, but with some small probability they jump longer distances through what we call global motion. For filtering, we decompose the posterior over local and global movements into two linked processes. The posterior over the global movements and measurement associations is sampled, while we track the local movement analytically using Kalman filters. This novel filter is evaluated on point cloud data gathered autonomously by a mobile robot over an extended period of time. We show that tracking jumping objects is feasible, and that the proposed probabilistic treatment outperforms previous methods when applied to real world data. The key to efficient probabilistic tracking in this scenario is focused sampling of the object posteriors.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2019. Vol. 35, no 1, p. 231-247
Keywords [en]
Dynamic mapping, mobile robot, movable objects, multitarget tracking (MTT), Rao-Blackwellized particle filter (RBPF), service robots
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-245151DOI: 10.1109/TRO.2018.2876111ISI: 000458197300017Scopus ID: 2-s2.0-85057204782OAI: oai:DiVA.org:kth-245151DiVA, id: diva2:1295883
Note

QC 20190313

Available from: 2019-03-13 Created: 2019-03-13 Last updated: 2019-03-18Bibliographically approved

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Bore, NilsEkekrantz, JohanJensfelt, PatricFolkesson, John

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