Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Simultaneous localization, mapping and moving object tracking
Simultaneous localization, mapping and moving object tracking
High-Integrity IMM-EKF-Based Road Vehicle Navigation With Low-Cost GPS/SBAS/INS
IEEE Transactions on Intelligent Transportation Systems
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This paper presents the problem of outdoor vehicle localization during unusual maneuvers with the Interacting Multiple Model (IMM) and Extended Kalman Filter (EKF) approaches. IMM, contrary to classical methods, is based on the discretization of the vehicle evolution space into simple maneuvers. Each maneuver is represented by a simple dynamic model such as a constant velocity or a constant turning model. This allows the method to be optimized for highly dynamic vehicles. In this work, we focus on unusual vehicle maneuvers during some special driving situations, including very strong accelerations, high speed turnings or backwards driving with stop stages. The presented results are based on real measurements collected from different scenarios. Based on an EKF vs. IMM comparison, these results show a real interest of using the IMM method in order to take into account unusual maneuvers.