摘要
针对室外环境中的机器人"绑架"问题,提出了基于地图匹配的SLAM方法.该方法舍弃了机器人里程计信息,只利用局部地图和全局地图的图形相关性进行机器人定位.方法的核心是多重估计数据关联,并将奇异值分解应用到机器人位姿计算中.利用Victoria Park数据集将本算法与基于扩展卡尔曼滤波器的方法进行比较,实验结果证明了本文提出的算法的有效性.
For the kidnapped robot problem in outdoor environment, a map matching based SLAM （simultaneous localization and mapping） solution is proposed. The odometer information is not involved in this method, and the robot localization is based on correlation between local and global maps. The core of the proposed method is multiple hypothesis data association. Singular value decomposition is also applied to robot pose calculation. The proposed method is compared with the EKF （extended Kalman filter） approach by Victoria Park dataset, and the experiment results prove the validity of the proposed method.
出处
《机器人》
EI
CSCD
北大核心
2010年第5期655-660,665,共7页
Robot
基金
机器人学国家重点实验室基金资助项目(R2200703)
关键词
SLAM
机器人“绑架”问题:地图匹配
SLAM （simultaneous localization and mapping）
kidnapped robot problem
map matching
