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This paper presents the Optimized Kalman Particle Swarm (OKPS) filter. This filter is a new robust data fusion approach adapted for ego-vehicle localization in degraded signal conditions. The OKPS is the improved version of the hybridization of the Particle Filter (PF) by Particle Swarm Optimization notions (PSO). Taking also some features from the Extended Kalman filter (EKF), the OKPS is designed...
This paper describes the development of a filter algorithm based on the behaviour of biological swarms. The main goal of the algorithm is to perform vehicle localization by combining the data from different sensors — GPS, IMU, speedometers, etc. — and digital maps. In this sense, the algorithm considers several solutions at the same time like Particles Filters. The algorithm has been developed off-line...
Estimating the configuration of a vehicle is crucial for navigation. The most classical approaches are (extended) Kalman filtering and Markov localization, often implemented via particle filtering. Interval analysis allows an alternative approach: bounded-error localization. Contrary to classical Extended Kalman Filtering, this approach allows global localisation, and contrary to Markov localization...
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