An efficient approach to odometric non-systematic error modeling for mobile robots

Odometric non-systematic error modeling for mobile robot is the basis of localization. Most of the approaches to odometric non-systematic error modeling are designed for some special driving-type robots nowadays. And the long-term odometric errors without bound, which degrade the localization precision after long-distance movement, are not often capable of being compensated in real-time. Therefore, a general approach to odometric nonsystematic error modeling for mobile robot is proposed in regard to both synchronous drive roller robots and differential drive roller robots. The approach assumes that the robot path is approximated to circular arcs. The function relationships, between the odometric process input and non-systematic errors, are derived on the basis of the odometric error transformation rules, further the accumulative errors of odometry in the localization process are compensated in real-time. The experiments show that the compensation of non-systematic error can reduce the odometric long-term errors efficiently, and improve the localization precision remarkably.