A Review of Point Cloud Registration Algorithms for Mobile Robotics
Identifieur interne : 000734 ( Main/Exploration ); précédent : 000733; suivant : 000735A Review of Point Cloud Registration Algorithms for Mobile Robotics
Auteurs : François Pomerleau [Canada] ; Francis Colas [France] ; Roland Siegwart [Suisse]Source :
- Foundations and Trends in Robotics [ 1935-8253 ] ; 2015.
Abstract
The topic of this review is geometric registration in robotics. Registration algorithms associate sets of data into a common coordinate system. They have been used extensively in object reconstruction, inspection, medical application, and localization of mobile robotics. We focus on mobile robotics applications in which point clouds are to be registered. While the underlying principle of those algorithms is simple, many variations have been proposed for many different applications. In this review, we give a historical perspective of the registration problem and show that the plethora of solutions can be organized and differentiated according to a few elements. Accordingly, we present a formalization of geometric registration and cast algorithms proposed in the literature into this framework. Finally, we review a few applications of this framework in mobile robotics that cover different kinds of platforms, environments, and tasks. These examples allow us to study the specific requirements of each use case and the necessary configuration choices leading to the registration implementation. Ultimately, the objective of this review is to provide guidelines for the choice of geometric registration configuration.
Url:
DOI: 10.1561/2300000035
Affiliations:
- Canada, France, Suisse
- Grand Est, Lorraine (région), Ontario
- Metz, Nancy, Toronto
- Université de Lorraine, Université de Toronto
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type="university">Université de Lorraine</orgName></affiliation></author><author><name sortKey="Siegwart, Roland" sort="Siegwart, Roland" uniqKey="Siegwart R" first="Roland" last="Siegwart">Roland Siegwart</name><affiliation wicri:level="1"><hal:affiliation type="laboratory" xml:id="struct-42457" status="VALID"><orgName>Autonomous Systems Lab</orgName><orgName type="acronym">ASL</orgName><desc><address><addrLine>Autonomous Systems Lab Institute of Robotics and Intelligent Systems ETH Zentrum, CLA E 31 Tannenstrasse 3 CH-8092 Zürich</addrLine><country key="CH"></country></address><ref type="url">http://www.asl.ethz.ch/</ref></desc><listRelation><relation active="#struct-300117" type="direct"></relation></listRelation><tutelles><tutelle active="#struct-300117" type="direct"><org type="institution" xml:id="struct-300117" status="VALID"><orgName>Swiss Federal Research Institute</orgName><desc><address><country key="FR"></country></address></desc></org></tutelle></tutelles></hal:affiliation><country>Suisse</country></affiliation></author></analytic><idno type="DOI">10.1561/2300000035</idno><series><title level="j">Foundations and Trends in Robotics</title><idno type="ISSN">1935-8253</idno><imprint><date type="datePub">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The topic of this review is geometric registration in robotics. Registration algorithms associate sets of data into a common coordinate system. They have been used extensively in object reconstruction, inspection, medical application, and localization of mobile robotics. We focus on mobile robotics applications in which point clouds are to be registered. While the underlying principle of those algorithms is simple, many variations have been proposed for many different applications. In this review, we give a historical perspective of the registration problem and show that the plethora of solutions can be organized and differentiated according to a few elements. Accordingly, we present a formalization of geometric registration and cast algorithms proposed in the literature into this framework. Finally, we review a few applications of this framework in mobile robotics that cover different kinds of platforms, environments, and tasks. These examples allow us to study the specific requirements of each use case and the necessary configuration choices leading to the registration implementation. Ultimately, the objective of this review is to provide guidelines for the choice of geometric registration configuration.</div></front></TEI><affiliations><list><country><li>Canada</li><li>France</li><li>Suisse</li></country><region><li>Grand Est</li><li>Lorraine (région)</li><li>Ontario</li></region><settlement><li>Metz</li><li>Nancy</li><li>Toronto</li></settlement><orgName><li>Université de Lorraine</li><li>Université de Toronto</li></orgName></list><tree><country name="Canada"><region name="Ontario"><name sortKey="Pomerleau, Francois" sort="Pomerleau, Francois" uniqKey="Pomerleau F" first="François" last="Pomerleau">François Pomerleau</name></region></country><country name="France"><region name="Grand Est"><name sortKey="Colas, Francis" sort="Colas, Francis" uniqKey="Colas F" first="Francis" last="Colas">Francis Colas</name></region></country><country name="Suisse"><noRegion><name sortKey="Siegwart, Roland" sort="Siegwart, Roland" uniqKey="Siegwart R" first="Roland" last="Siegwart">Roland Siegwart</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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