标签：rri effective tac 情况 int 传统 capture urb 缺点
A Multi-Position Joint Particle Filtering Method for Vehicle Localization in Urban Area
Shuxia Gu, Zhiyu Xiang*, Yi Zhang and Qi Qian
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 ?exible road map based probabilistic ?ltering 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 ?exible 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.
泡泡一分钟：A Multi-Position Joint Particle Filtering Method for Vehicle Localization in Urban Area
标签：rri effective tac 情况 int 传统 capture urb 缺点
原文地址：https://www.cnblogs.com/feifanrensheng/p/10289218.html
