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Large scale multiple robot visual mapping with heterogeneous landmarks in semi-structured terrain
Australian Centre for Field Robotics, University of Sydney, NSW, Australia. (Center for Autonomous System)
CNRS; LAAS; Toulouse, France and Université de Toulouse; UPS, INSA, INP, ISAE; LAAS; Toulouse, France.ORCID iD: 0000-0003-3011-1505
CNRS; LAAS; Toulouse, France and Université de Toulouse; UPS, INSA, INP, ISAE; LAAS; Toulouse, France. (RIS)
CNRS; LAAS; Toulouse, France and Université de Toulouse; UPS, INSA, INP, ISAE; LAAS; Toulouse, France. (RIS)
2011 (English)In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 59, no 9, p. 654-674Article in journal (Refereed) Published
Abstract [en]

This paper addresses the cooperative localization and visual mapping problem with multiple heterogeneous robots. The approach is designed to deal with the challenging large semi-structured outdoors environments in which aerial/ground ensembles are to evolve. We propose the use of heterogeneous visual landmarks, points and line segments, to achieve effective cooperation in such environments. A large-scale SLAM algorithm is generalized to handle multiple robots, in which a global graph maintains the relative relationships between a series of local sub-maps built by the different robots. The key issue when dealing with multiple robots is to find the link between them, and to integrate these relations to maintain the overall geometric consistency; the events that introduce these links on the global graph are described in detail. Monocular cameras are considered as the primary extereoceptive sensor. In order to achieve the undelayed initialization required by the bearing-only observations, the well-known inverse-depth parametrization is adopted to estimate 3D points. Similarly, to estimate 3D line segments, we present a novel parametrization based on anchored Plücker coordinates, to which extensible endpoints are added. Extensive simulations show the proposed developments, and the overall approach is illustrated using real-data taken with a helicopter and a ground rover.

Place, publisher, year, edition, pages
Elsevier, 2011. Vol. 59, no 9, p. 654-674
Keywords [en]
Multi-robotscooperation, VisualSLAM
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-73413DOI: 10.1016/j.robot.2011.05.008ISI: 000293804000005OAI: oai:DiVA.org:liu-73413DiVA, id: diva2:472016
Available from: 2012-01-03 Created: 2012-01-03 Last updated: 2018-01-12Bibliographically approved

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Berger, Cyrille

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