Abstract
This paper presents the Optimized Kalman Particle Swarm (OKPS) filter. This filter results from two years of research and improves the Swarm Particle Filter (SPF). The OKPS has been designed to be both cooperative and reactive. It combines the advantages of the Particle Filter (PF) and the metaheuristic Particle Swarm Optimization (PSO) for ego-vehicles localization applications. In addition to a simple fusion between the swarm optimization and the particular filtering (which leads to the Swarm Particle Filter), the OKPS uses some attributes of the Extended Kalman filter (EKF). The OKPS filter innovates by fitting its particles with a capacity of self-diagnose by means of the EKF covariance uncertainty matrix. The particles can therefore evolve by exchanging information to assess the optimized position of the ego-vehicle. The OKPS fuses data coming from embedded sensors (low cost INS, GPS and Odometer) to perform a robust ego-vehicle positioning. The OKPS is compared to the EKF filter and to filters using particles (PF and SPF) on real data from our equipped vehicle.
Highlights
Localization is a key technological component for any Advanced Driver Assistance System (ADAS)
Instead of Swarm Particle Filter (SPF) particles weighting which is only relative to the GPS position, this new adaptive weighting allows inertial behavior avoiding particles sticking to GPS
The filters: Extended Kalman Filter (EKF), Particle Filter (PF), SPF and Optimized Kalman Particle Swarm (OKPS) are tested in an ego-vehicle localization application
Summary
Localization is a key technological component for any Advanced Driver Assistance System (ADAS). The particles move independently toward the region where the positioning probability is higher (given the sensor information) Particles optimize their poses by evolving toward their best neighbors (social communication) to iteratively converge to local optima or a global optimum. The resulting algorithm is a PSO aided EKF which needs anyway a PSO parameters tuning We inspired from it and introduced an innovative localization method for vehicles. The Optimized Kalman Particle Swarm (OKPS) performs the vehicle real-time positioning considering a dynamic optimization problem. The OKPS intends to minimize the parameterization needs while taking advantages of PSO/EKF/PF hybrid approach All these approaches are compared and ranked in terms of accuracy and robustness using real word experimental data of driving scenarios on the Satory-Versailles test track.
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