A Low-cost GNSS/IMU/Visual monoSLAM/WSS Integration Based on Federated Kalman Filtering for Navigation in Urban Environments
Résumé
Car navigation performance improvement is a subject of great interest nowadays especially with the development of autonomous car navigation. In urban environments, it is often difficult to rely on standalone Global Navigation Satellite System (GNSS) to obtain continuously an accurate and reliable navigation solution. In fact, the presence of buildings and other structures hindering the reception of GNSS signals (blockage, multipath, NLOS, poor geometry, etc.) makes it difficult for GNSS to provide accurate, continuous and reliable navigation solution in such an environment. A possible solution for this problem is to fuse information from a limited number of GNSS measurements and other sensors in order to enhance the system performance in terms of accuracy and availability. In this paper, we propose an integrated navigation system that fuses different sensor information in order to improve the car navigation performance in urban environments. A Low-cost navigation solution is proposed since the intended application is cost-sensitive. The proposed solution integrates information from an Inertial Measurement Unit (IMU), a GNSS receiver, a Wheel Speed Sensor (WSS) and a vision module based on monocular Simultaneous Localization And Mapping (SLAM). Motion constraints related to the movement of a land vehicle on the ground are also taken into account.
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