Robust autonomous robot localization using interval analysis

Michel Kieffer (1, 2, 3) , Luc Jaulin (4, 5, 6) , Eric Walter (1) , Dominique Meizel (6)

1 L2S - Laboratoire des signaux et systèmes
2 LTCI - Laboratoire Traitement et Communication de l'Information
3 IUF - Institut universitaire de France
4 STIC - Département STIC [Brest]
5 Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
6 LISA - Laboratoire d'Ingéniérie des Systèmes Automatisés

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 The third limitation is their lack of robustness to outliers due, e.g., to sensor malfunctions or outdated maps.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.

Mots clés: Interval Analysis; Identification; State Estimation; Outliers; Bounded Errors; Robotics

Domaines: Sciences de l'ingénieur [physics]

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⟩