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Incorporating Ego-motion Uncertainty Estimates in Range Data Registration
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0002-2953-1564
Örebro University, School of Science and Technology. (AASS)
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0002-6013-4874
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0001-8658-2985
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2017 (English)Conference 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
2017.
National Category
Robotics
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-62803OAI: oai:DiVA.org:oru-62803DiVA: diva2:1159885
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017), Vancouver, Canada, September 24–28, 2017
Projects
Semantic RobotsILIAD
Funder
Knowledge Foundation
Available from: 2017-11-24 Created: 2017-11-24 Last updated: 2017-11-28Bibliographically approved

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Andreasson, HenrikAdolfsson, DanielStoyanov, TodorMagnusson, MartinLilienthal, Achim

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