Abstract: Odometric non-systematic error modeling for mobile robot is the basis for researching the positioning of mobile robot. Most of the approaches to odometric non-systematic error modeling are designed for some special driving-type robot up to now. And the odometric long term errors without bound, which degrade positioning precision after long-distance movement, are not often capable of being compensated in real-time. Therefore, a general approach to odometric non-systematic error modeling for mobile robot is proposed with respect to both synchronous-drive robot and differential-drive robot. The method presents an assumption that the robot path is approximated to circular arcs. The approximate function relationships between the process input of odometry and non-systematic error are derived based on the odometric error transformations rules, further the approach is applied to pose tracking for mobile robots, which is online used to compensate the accumulative errors of odometry in the process of positioning. The contrast experiments before and after odometric non-systematic error modeling denote that the new approach reduces the accumulative error of odometry and improves the positioning precision efficiently.

Key words: Extended Kalman filter, Non-systematic error modeling, Pose estimation, Positioing mobile robot