A real time visual SLAM for RGB-D cameras based on chamfer distance and occupancy grid
Identifieur interne : 000849 ( Hal/Curation ); précédent : 000848; suivant : 000850A real time visual SLAM for RGB-D cameras based on chamfer distance and occupancy grid
Auteurs : Abdallah Dib [France] ; Nicolas Beaufort [France] ; François Charpillet [France]Source :
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Abstract
We present a feature based visual SLAM method that uses chamfer distance to estimate the camera motion from RGB-D images. The proposed method does not require any matching which is an expensive operation and always generates false matching that affects the estimated camera motion. Our approach registers the input image iteratively by minimizing the distance between the feature points and the occupancy grid using a distance map. We demonstrate with real experiments the capability of the method to build accurate 3D map of the environment with a hand-held camera. While the system was mainly developed to work with RGB-D camera, occupancy grid representation gives the method the ability to work with various types of sensors, we show the capacity of the system to construct accurate 2D maps using telemeter data. We also discuss the similarities between the proposed approach and the traditional ICP algorithm.
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DOI: 10.1109/AIM.2014.6878153
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BP 10578153 Le Chesnay Cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/en/</ref></desc></org></tutelle><tutelle active="#struct-413289" type="direct"><org type="institution" xml:id="struct-413289" status="VALID"><idno type="IdRef">157040569</idno><idno type="IdUnivLorraine">[UL]100--</idno><orgName>Université de Lorraine</orgName><orgName type="acronym">UL</orgName><date type="start">2012-01-01</date><desc><address><addrLine>34 cours Léopold - CS 25233 - 54052 Nancy cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.univ-lorraine.fr/</ref></desc></org></tutelle><tutelle name="UMR7503" active="#struct-441569" type="direct"><org type="institution" xml:id="struct-441569" status="VALID"><idno type="ISNI">0000000122597504</idno><idno type="IdRef">02636817X</idno><orgName>Centre National de la Recherche Scientifique</orgName><orgName type="acronym">CNRS</orgName><date type="start">1939-10-19</date><desc><address><country key="FR"></country></address><ref type="url">http://www.cnrs.fr/</ref></desc></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><settlement type="city">Metz</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université de Lorraine</orgName></affiliation></author></analytic><idno type="DOI">10.1109/AIM.2014.6878153</idno></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>ICP</term><term>chamfer distance</term><term>occupancy grid</term><term>rgb-d</term><term>rgbd cameras</term><term>slam</term><term>visual odometry</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">We present a feature based visual SLAM method that uses chamfer distance to estimate the camera motion from RGB-D images. The proposed method does not require any matching which is an expensive operation and always generates false matching that affects the estimated camera motion. Our approach registers the input image iteratively by minimizing the distance between the feature points and the occupancy grid using a distance map. We demonstrate with real experiments the capability of the method to build accurate 3D map of the environment with a hand-held camera. While the system was mainly developed to work with RGB-D camera, occupancy grid representation gives the method the ability to work with various types of sensors, we show the capacity of the system to construct accurate 2D maps using telemeter data. We also discuss the similarities between the proposed approach and the traditional ICP algorithm.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">A real time visual SLAM for RGB-D cameras based on chamfer distance and occupancy grid</title><author role="aut"><persName><forename type="first">Abdallah</forename><surname>Dib</surname></persName><email></email><idno type="halauthor">1104197</idno><orgName ref="#struct-300009"></orgName><affiliation ref="#struct-415996"></affiliation><affiliation ref="#struct-206040"></affiliation></author><author role="aut"><persName><forename type="first">Nicolas</forename><surname>Beaufort</surname></persName><email></email><idno type="halauthor">590649</idno><orgName ref="#struct-300009"></orgName><affiliation ref="#struct-415996"></affiliation></author><author role="aut"><persName><forename type="first">François</forename><surname>Charpillet</surname></persName><email>francois.charpillet@loria.fr</email><ptr type="url" target="http://www.loria.fr/~charp"></ptr><idno type="idhal">francois-charpillet</idno><idno type="halauthor">800899</idno><affiliation ref="#struct-206040"></affiliation><affiliation ref="#struct-415996"></affiliation></author><editor role="depositor"><persName><forename>dib</forename><surname>abdallah</surname></persName><email>abdallah.dib@inria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2014-12-04 14:40:04</date><date type="whenModified">2015-12-19 01:05:30</date><date type="whenReleased">2014-12-04 16:45:53</date><date type="whenProduced">2014-07-08</date><date type="whenEndEmbargoed">2014-12-04</date><ref type="file" target="https://hal.inria.fr/hal-01090998/document"><date notBefore="2014-12-04"></date></ref><ref type="file" subtype="author" n="1" target="https://hal.inria.fr/hal-01090998/file/RealTimeVisualSLAM.pdf"><date notBefore="2014-12-04"></date></ref></edition><respStmt><resp>contributor</resp><name key="304280"><persName><forename>dib</forename><surname>abdallah</surname></persName><email>abdallah.dib@inria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">hal-01090998</idno><idno type="halUri">https://hal.inria.fr/hal-01090998</idno><idno type="halBibtex">dib:hal-01090998</idno><idno type="halRefHtml">IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Jul 2014, Besançon, France. pp.652 - 657, 2014, <10.1109/AIM.2014.6878153></idno><idno type="halRef">IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Jul 2014, Besançon, France. pp.652 - 657, 2014, <10.1109/AIM.2014.6878153></idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</idno><idno type="stamp" n="INRIA-NANCY-GRAND-EST">INRIA Nancy - Grand Est</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="INRIA_TEST">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="INRIA2">INRIA 2</idno></seriesStmt><notesStmt><note type="audience" n="2">International</note><note type="invited" n="0">No</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note><note type="proceedings" n="0">No</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">A real time visual SLAM for RGB-D cameras based on chamfer distance and occupancy grid</title><author role="aut"><persName><forename type="first">Abdallah</forename><surname>Dib</surname></persName><idno type="halAuthorId">1104197</idno><orgName ref="#struct-300009"></orgName><affiliation ref="#struct-415996"></affiliation><affiliation ref="#struct-206040"></affiliation></author><author role="aut"><persName><forename type="first">Nicolas</forename><surname>Beaufort</surname></persName><idno type="halAuthorId">590649</idno><orgName ref="#struct-300009"></orgName><affiliation ref="#struct-415996"></affiliation></author><author role="aut"><persName><forename type="first">François</forename><surname>Charpillet</surname></persName><email>francois.charpillet@loria.fr</email><ptr type="url" target="http://www.loria.fr/~charp"></ptr><idno type="idHal">francois-charpillet</idno><idno type="halAuthorId">800899</idno><affiliation ref="#struct-206040"></affiliation><affiliation ref="#struct-415996"></affiliation></author></analytic><monogr><meeting><title>IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)</title><date type="start">2014-07-08</date><date type="end">2014-07-11</date><settlement>Besançon</settlement><country key="FR">France</country></meeting><imprint><biblScope unit="pp">652 - 657</biblScope><date type="datePub">2014-07-08</date></imprint></monogr><idno type="doi">10.1109/AIM.2014.6878153</idno></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="en">chamfer distance</term><term xml:lang="en">occupancy grid</term><term xml:lang="en">rgbd cameras</term><term xml:lang="en">slam</term><term xml:lang="en">rgb-d</term><term xml:lang="en">visual odometry</term><term xml:lang="en">ICP</term></keywords><classCode scheme="halDomain" n="info.info-rb">Computer Science [cs]/Robotics [cs.RO]</classCode><classCode scheme="halDomain" n="info.info-ai">Computer Science [cs]/Artificial Intelligence [cs.AI]</classCode><classCode scheme="halDomain" n="info.info-cv">Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">We present a feature based visual SLAM method that uses chamfer distance to estimate the camera motion from RGB-D images. The proposed method does not require any matching which is an expensive operation and always generates false matching that affects the estimated camera motion. Our approach registers the input image iteratively by minimizing the distance between the feature points and the occupancy grid using a distance map. We demonstrate with real experiments the capability of the method to build accurate 3D map of the environment with a hand-held camera. While the system was mainly developed to work with RGB-D camera, occupancy grid representation gives the method the ability to work with various types of sensors, we show the capacity of the system to construct accurate 2D maps using telemeter data. We also discuss the similarities between the proposed approach and the traditional ICP algorithm.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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