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Robust autonomous robot localization using interval analysis

Abstract : This paper deals with the determination of the position and orientation of a mobile robot from distance measurements provided by a belt of onboard ultrasonic sensors. The environment is assumed to be two-dimensional, and a map of its landmarks is available to the robot. In this context, classical localization methods have three main limitations. First, each data point provided by a sensor must be associated with a given landmark. This data-association step turns out to be extremely complex and time-consuming, and its results can usually not be guaranteed. The second limitation is that these methods are based on linearization, which makes them inherently local. The third limitation is their lack of robustness to outliers due, e.g., to sensor malfunctions or outdated maps. By contrast, the method proposed here, based on interval analysis, bypasses the data-association step, handless the problem as nonlinera and in a global way and is (extraordinarily) robust to outliers.
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Submitted on : Tuesday, July 16, 2013 - 11:04:24 AM
Last modification on : Wednesday, October 20, 2021 - 3:19:05 AM
Long-term archiving on: : Thursday, October 17, 2013 - 4:15:48 AM


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  • HAL Id : hal-00844915, version 1


Michel Kieffer, Luc Jaulin, Eric Walter, Dominique Meizel. Robust autonomous robot localization using interval analysis. Reliable Computing Journal, 2000, 3 (6), pp.337-361. ⟨hal-00844915⟩



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