A Multi-Position Joint Particle Filtering Method for Vehicle Localization in Urban Area

Abstract

Robust localization is a prerequisite for autonomous vehicles. Traditional visual localization methods like visual odometry suffer error accumulation on long range navigation. In this paper, a flexible road map based probabilistic filtering method is proposed to tackle this problem. To effectively match the ego-trajectory to various curving roads in map, a new representation based on anchor point (AP) which captures the main curving points on the trajectory is presented. Based on APs of the map and trajectory, a flexible Multi-Position Joint Particle Filtering (MPJPF) framework is proposed to correct the position error. The method features the capability of adaptively estimating a series of APs jointly and only updates the estimation at situations with low uncertainty. It explicitly avoids the drawbacks of obliging to determine the current position at large uncertain situations such as dense parallel road branches. The experiments carried out on KITTI benchmark demonstrate our success.