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Incorporating Ego-motion Uncertainty Estimates in Range Data Registration
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS)ORCID-id: 0000-0002-2953-1564
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS)
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS)ORCID-id: 0000-0002-6013-4874
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS)ORCID-id: 0000-0001-8658-2985
Vise andre og tillknytning
2017 (engelsk)Inngår i: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2017, s. 1389-1395Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2017. s. 1389-1395
Serie
Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems, ISSN 2153-0858, E-ISSN 2153-0866
HSV kategori
Forskningsprogram
Datavetenskap
Identifikatorer
URN: urn:nbn:se:oru:diva-62803DOI: 10.1109/IROS.2017.8202318ISI: 000426978201108Scopus ID: 2-s2.0-85041958720ISBN: 978-1-5386-2682-5 (digital)ISBN: 978-1-5386-2683-2 (tryckt)OAI: oai:DiVA.org:oru-62803DiVA, id: diva2:1159885
Konferanse
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017), Vancouver, Canada, September 24–28, 2017
Prosjekter
Semantic RobotsILIAD
Forskningsfinansiär
Knowledge FoundationEU, Horizon 2020, 732737Tilgjengelig fra: 2017-11-24 Laget: 2017-11-24 Sist oppdatert: 2018-04-09bibliografisk kontrollert

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Andreasson, HenrikAdolfsson, DanielStoyanov, TodorMagnusson, MartinLilienthal, Achim

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