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Improving Point-Cloud Accuracy from a Moving Platform in Field Operations
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems ( AASS ))
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems ( AASS ))ORCID iD: 0000-0001-8658-2985
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems ( AASS ))ORCID iD: 0000-0002-6013-4874
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems ( AASS ))ORCID iD: 0000-0003-0217-9326
2013 (English)In: 2013 IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings, 2013, 733-738 p.Conference paper, Published 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.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2013. 733-738 p.
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-30529DOI: 10.1109/ICRA.2013.6630654ISI: 000337617300107Scopus ID: 2-s2.0-84887272249ISBN: 978-1-4673-5641-1 (print)ISBN: 978-1-4673-5643-5 (print)OAI: oai:DiVA.org:oru-30529DiVA: diva2:644368
Conference
2013 IEEE International Conference on Robotics and Automation (ICRA, 6-10 May, Karlsruhe
Available from: 2013-08-30 Created: 2013-08-30 Last updated: 2017-10-18Bibliographically approved

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Almqvist, HåkanMagnusson, MartinStoyanov, TodorLilienthal, Achim J.
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CiteExportLink to record
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