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Conference papers

Set-membership state estimation by solving data association

Simon Rohou 1 Benoît Desrochers 2 Luc Jaulin 1
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : This paper deals with the localization problem of a robot in an environment made of indistinguishable landmarks, and assuming the initial position of the vehicle is unknown. This scenario is typically encountered in underwater applications for which landmarks such as rocks all look alike. Furthermore, the position of the robot may be lost during a diving phase, which obliges us to consider unknown initial position. We propose a deterministic approach to solve simultaneously the problems of data association and state estimation, without combinatorial explosion. The efficiency of the method is shown on an actual experiment involving an underwater robot and sonar data.
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Submitted on : Wednesday, July 22, 2020 - 11:59:51 AM
Last modification on : Monday, April 4, 2022 - 9:28:30 AM
Long-term archiving on: : Tuesday, December 1, 2020 - 4:20:23 AM


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  • HAL Id : hal-02904517, version 1


Simon Rohou, Benoît Desrochers, Luc Jaulin. Set-membership state estimation by solving data association. IEEE International Conference on Robotics and Automation (ICRA), May 2020, Paris, France. ⟨hal-02904517⟩



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