期刊
IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION
卷 19, 期 4, 页码 749-755出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TRA.2003.814500
关键词
autonomous vehicles; Kalman filter; map building; navigation
This paper presents new algorithms to implement simultaneous localization and mapping in environments with very large numbers of features. The algorithms present an efficient solution to the full update required by the compressed extended Kalman filter algorithm. It makes use of the relative landmark representation to develop very close to optimal decorrelation solutions. With this approach, the memory and computational requirements are reduced from similar to O(N-2) to similar to O(N*N-a), N and N-a proportional to the number of features in the map and features close to the vehicle, respectively. Experimental results are presented to verify the operation of the system when working in large outdoor environments.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据