In this paper, an innovative collaborative data fusion approach to ego-vehicle localization is presented. This approach called Optimized Kalman Swarm (OKS) is a data fusion and filtering method, fusing data from a low...In this paper, an innovative collaborative data fusion approach to ego-vehicle localization is presented. This approach called Optimized Kalman Swarm (OKS) is a data fusion and filtering method, fusing data from a low cost GPS, an INS, an Odometer and a Steering wheel angle encoder. The OKS is developed addressing the challenge of managing reactivity and robustness during a real time ego-localization process. For ego-vehicle localization, especially for highly dynamic on-road maneuvers, a filter needs to be robust and reactive at the same time. In these situations, the balance between reactivity and robustness concepts is crucial. The OKS filter represents an intelligent cooperative-reactive localization algorithm inspired by dynamic Particle Swarm Optimization (PSO). It combines advantages coming from two filters: Particle Filter (PF) and Extended Kalman filter (EKF). The OKS is tested using real embedded sensors data collected in the Satory’s test tracks. The OKS is also compared with both the well-known EKF and the Particle Filters (PF). The results show the efficiency of the OKS for a high dynamic driving scenario with damaged and low quality GPS data.展开更多
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...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.展开更多
Interval constraint propagation (ICP) algorithms allow to solve problems described as constraint satisfaction problems (CSP). ICP has been successfully applied to vehicle localization in the last few years. Once the l...Interval constraint propagation (ICP) algorithms allow to solve problems described as constraint satisfaction problems (CSP). ICP has been successfully applied to vehicle localization in the last few years. Once the localization problem has been stated, a large class of ICP solvers can be used. This paper compares a few ICP algorithms, using the same experimental data, in order to rank their performances in terms of accuracy and computing time.展开更多
文摘In this paper, an innovative collaborative data fusion approach to ego-vehicle localization is presented. This approach called Optimized Kalman Swarm (OKS) is a data fusion and filtering method, fusing data from a low cost GPS, an INS, an Odometer and a Steering wheel angle encoder. The OKS is developed addressing the challenge of managing reactivity and robustness during a real time ego-localization process. For ego-vehicle localization, especially for highly dynamic on-road maneuvers, a filter needs to be robust and reactive at the same time. In these situations, the balance between reactivity and robustness concepts is crucial. The OKS filter represents an intelligent cooperative-reactive localization algorithm inspired by dynamic Particle Swarm Optimization (PSO). It combines advantages coming from two filters: Particle Filter (PF) and Extended Kalman filter (EKF). The OKS is tested using real embedded sensors data collected in the Satory’s test tracks. The OKS is also compared with both the well-known EKF and the Particle Filters (PF). The results show the efficiency of the OKS for a high dynamic driving scenario with damaged and low quality GPS data.
文摘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.
文摘Interval constraint propagation (ICP) algorithms allow to solve problems described as constraint satisfaction problems (CSP). ICP has been successfully applied to vehicle localization in the last few years. Once the localization problem has been stated, a large class of ICP solvers can be used. This paper compares a few ICP algorithms, using the same experimental data, in order to rank their performances in terms of accuracy and computing time.
