Papers
14
Total Citations
170
H-Index
8
About
Philippe Bonnifait is a prominent researcher specializing in mobile robotics, autonomous vehicle localization, and sensor fusion. His work has fundamentally advanced how robots and autonomous vehicles determine their position and navigate complex environments with reliability and precision. Bonnifait's most influential contribution, his 1998 paper on odometric and goniometric localization (58 citations), established a rigorous framework for combining wheel odometry with landmark-bearing measurements, supported by formal observability analysis. This foundational work was extended through real-time experimental validation using extended Kalman filtering, demonstrating practical deployment on outdoor vehicles. A recurring theme in his research is the quest for guaranteed, integrity-assured localization — addressing the limitations of classical Kalman filtering through interval analysis and constraint propagation techniques, ensuring bounded error estimates critical for safety-sensitive applications. His later contributions broadened into evidential and Dempster-Shafer frameworks for occupancy grid mapping, enabling richer uncertainty management in dynamic environments, including the integration of semantic lane information from geo-referenced maps for autonomous driving. His 2018 work on box particle filtering extended set-membership methods to simultaneous localization and mapping (SLAM). Collectively, Bonnifait's research portfolio reflects a sustained commitment to robust, uncertainty-aware perception systems that underpin safe autonomous navigation.
Research Focus
Key Achievements
Top Papers
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- 4Evidential Grids Information Management In Dynamic Environments11 citations · 2014
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- 6Box Particle Filtering for SLAM with Bounded Errors9 citations · 2018
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- 9Mobile robots cooperation with biased exteroceptive measurements7 citations · 2014
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