Square Root Sigma Point Kalman Filter Based SLAM
Mobile robot Simultaneous Localization and Mapping (SLAM) problem is one of the most active research areas in robotics. Sigma Point Kalman Filter (SPKF), including Unscented Kalman Filter (UKF) and Central Difference Kalman Filter (CDKF), has been widely apphed for SLAM. In this paper, an improved SPKF, Square Root SPKF (SR-SPKF), is applied for SLAM and a new SR-CDKF based SLAM algorithm is proposed. The SLAM accuracy, consistency and running time of UKF, SR-UKF, CDKF and SR-CDKF based SLAM algorithms are analyzed and compared by simulation. Results show that SR-SPKF based SLAM has almost the same accuracy and consistency as SPKF based one, while with less running time as it updates the square root of the state covariance directly. The SR-CDKF based SLAM shows the best performance among the four compared algorithms.
SLAM SPKF SR-CDKF Accuracy Consistency
Chen Chen Yinhang Cheng
School of Electronics and Information Engineering Beijing Jiaotong University Beijing, China
国际会议
重庆
英文
1554-1558
2011-08-20（万方平台首次上网日期，不代表论文的发表时间）