Set-membership state estimation by solving data association

Simon Rohou, BenoƮt Desrochers, Luc Jaulin
Virtually presented at ICRA Conference, Paris, 2020

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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.

Keywords: mobile robotics, localization, data association, underwater robotics, sonars, interval analysis, contractors, tubes

Dataset (Daurade mission)

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Tubex library

See the Tubex project.