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Beyond points: Evaluating recent 3D scan-matching algorithms
Örebro University, School of Science and Technology, Örebro University, Sweden. (AASS MRO Lab)ORCID iD: 0000-0001-8658-2985
Deptartment of EECS, Jacobs University, Bremen, Germany.
Örebro University, School of Science and Technology, Örebro University, Sweden. (AASS MRO Lab)ORCID iD: 0000-0002-6013-4874
Deptartment of EECS, Jacobs University, Bremen, Germany.
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2015 (English)In: 2015 IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings , 2015, Vol. 2015 June, 3631-3637 p.Conference 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. Vol. 2015 June, 3631-3637 p.
Series
Proceedings - IEEE International Conference on Robotics and Automation, ISSN 1050-4729 ; 2015-June
Keyword [en]
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 Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-45597DOI: 10.1109/ICRA.2015.7139703ISI: 000370974903093Scopus ID: 2-s2.0-84938280438ISBN: 978-1-4799-6923-4 (print)OAI: oai:DiVA.org:oru-45597DiVA: diva2:847086
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: 2017-03-06Bibliographically approved

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