Publication
Map-matching algorithms for robot self-localization: a comparison between perfect match, iterative closest point and normal distributions transform
| dc.contributor.author | Sobreira, Héber | |
| dc.contributor.author | Costa, Carlos M. | |
| dc.contributor.author | Sousa, Ivo | |
| dc.contributor.author | Rocha, Luís Freitas | |
| dc.contributor.author | Lima, José | |
| dc.contributor.author | Farias, P.C.M.A. | |
| dc.contributor.author | Costa, Paulo Gomes da | |
| dc.contributor.author | Moreira, António Paulo G. M. | |
| dc.date.accessioned | 2018-03-12T12:35:06Z | |
| dc.date.available | 2018-03-12T12:35:06Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | The self-localization of mobile robots is one of the most fundamental problems in the robotics navigation eld. It is a complex and challenging issue due to the hard requirements that autonomous mobile vehicles are subject to, particularly with regard to the algorithms accuracy, robustness and computational e ciency. In this paper, we present a comparison of the three most used map-matching algorithms for robot self-localization based on natural landmarks, namely our implementation of the Perfect Match (PM) and the Iterative Closest Point (ICP) along with the Normal Distribution Transform (NDT) available in the Point Cloud Library (PCL). Regarding the ICP algorithm, we introduce in this paper a new methodology for performing correspondence estimation using lookup tables that was inspired in the PM approach. This new method for computing the closest map point to a given sensor reading proved to be 40 to 60 times faster than the existing k-d tree approach used in the PCL implementation and allowed the ICP algorithm to perform point cloud registration 5 to 9 times faster. For the purpose of comparing the presented algorithms we have considered a set of representative metrics, such as the pose estimation accuracy, the computational e ciency, the convergence speed, the maximum admissible initialization error and the robustness to the presence of outliers in the robots sensors data. The test results were retrieved using our ROS natural landmark public dataset that contains several tests with simulated and real sensor data. The performance and robustness of the Perfect Match is highlighted throughout this article, showing its advantage for real-time embedded systems with limited computing power which require accurate pose estimation and fast reaction times when the robot is navigating at high speeds. | pt_PT |
| dc.description.sponsorship | The research leading to these results has received fund- ing from the European Union’s Horizon 2020 - The EU Framework Programme for Research and Innovation 2014-2020, under grant agreement No. 688807. Project ”TEC4Growth - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020” is financed by the North Portugal Regional Operational. Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF). This work is also financed by the ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme, and by National Fund through the FCT Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project POCI-01-0145-FEDER-006961. This work is financed by the ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme, and by National Funds through the Portuguese funding agency, FCT -Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology), within project SAICTPAC/0034/2015-POCI-01-0145-FEDER-016418. The research leading to these results has received funding from the European Unions Horizon 2020 - The EU Framework Programme for Research and Innovation 20142020, under grant agreement No. 688807 ColRobot. P. C. M. A. Farias acknowledge support from CNPq/CsF PDE 233517/2014-6 for providing a scholarship. | |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Sobreira, Héber; Costa, Carlos M.; Sousa, Ivo; Rocha, Luis; Lima, José; Farias, P. C. M. A.; Costa, Paulo; Moreira, A. Paulo (2019). Map-matching algorithms for robot self-localization: a comparison between perfect match, iterative closest point and normal distributions transform. Journal of Intelligent and Robotic Systems. ISSN 0921-0296. 93:3-4, p. 533-546 | pt_PT |
| dc.identifier.doi | 10.1007/s10846-017-0765-5 | pt_PT |
| dc.identifier.issn | 0921-0296 | |
| dc.identifier.uri | http://hdl.handle.net/10198/16260 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.relation | Collaborative Robotics for Assembly and Kitting in Smart Manufacturing | |
| dc.relation | Aligning Manufacturing Decision Making with Advanced Manufacturing Technologies. | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | 2D laser scan | pt_PT |
| dc.subject | Map matching | pt_PT |
| dc.subject | Robot self-localization | pt_PT |
| dc.title | Map-matching algorithms for robot self-localization: a comparison between perfect match, iterative closest point and normal distributions transform | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Collaborative Robotics for Assembly and Kitting in Smart Manufacturing | |
| oaire.awardTitle | Aligning Manufacturing Decision Making with Advanced Manufacturing Technologies. | |
| oaire.awardURI | info:eu-repo/grantAgreement/EC/H2020/688807/EU | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/9471 - RIDTI/SAICTPAC%2F0034%2F2015/PT | |
| oaire.citation.endPage | 14 | pt_PT |
| oaire.citation.startPage | 1 | pt_PT |
| oaire.citation.title | Journal of Intelligent and Robotic Systems | pt_PT |
| oaire.fundingStream | H2020 | |
| oaire.fundingStream | 9471 - RIDTI | |
| person.familyName | Lima | |
| person.givenName | José | |
| person.identifier | R-000-8GD | |
| person.identifier.ciencia-id | 6016-C902-86A9 | |
| person.identifier.orcid | 0000-0001-7902-1207 | |
| person.identifier.rid | L-3370-2014 | |
| person.identifier.scopus-author-id | 55851941311 | |
| project.funder.identifier | http://doi.org/10.13039/501100008530 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | European Commission | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | restrictedAccess | pt_PT |
| rcaap.type | article | pt_PT |
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