Gaël Pages — accepted AI-conference papers

2022

Maintaining Robot Localizability with Bayesian Cramér-Rao Lower Bounds

Justin Cano, Corentin Chauffaut, Eric Chaumette, Gaël Pages, Jerome Le Ny

IROS 2022poster

Accurate and real-time position estimates are cru-cial for mobile robots. This work focuses on ranging-based positioning systems, which rely on distance measurements between known points, called anchors, and a tag to localize. The topology of the network formed by the anchors strongly influences the…

Cited by 1 Source ↗Scholar ↗

2022

Optimal Localizability Criterion for Positioning with Distance-Deteriorated Relative Measurements

Justin Cano, Gaël Pages, Eric Chaumette, Jerome Le Ny

IROS 2022poster

Position estimation in Multi-Robot Systems (MRS) relies on relative angle or distance measurements between the robots, which generally deteriorate as distances increase. Moreover, the localization accuracy is strongly influenced both by the quality of the raw measurements but also by the overall geo…

Cited by 4 Source ↗Scholar ↗