Beyond points: Evaluating recent 3D scan-matching algorithms
2015 (English)In: 2015 IEEE International Conference on Robotics and Automation (ICRA), Institute of Electrical and Electronics Engineers Inc. , 2015, Vol. 2015 June, 3631-3637 p.Conference paper (Refereed)
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
Institute of Electrical and Electronics Engineers Inc. , 2015. Vol. 2015 June, 3631-3637 p.
, Proceedings - IEEE International Conference on Robotics and Automation, ISSN 1050-4729 ; 2015-June
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
Research subject Computer Science
IdentifiersURN: urn:nbn:se:oru:diva-45597DOI: 10.1109/ICRA.2015.7139703ISI: 000370974903093ScopusID: 2-s2.0-84938280438ISBN: 978-1-4799-6923-4OAI: oai:DiVA.org:oru-45597DiVA: diva2:847086
2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, USA, May 26-30, 2015
FunderKnowledge Foundation, 20110214EU, FP7, Seventh Framework Programme, ICT-2011-600877EU, FP7, Seventh Framework Programme, ICT-270350