一种新的移动机器人Monte Carlo自主定位算法

New Monte Carlo algorithm for mobile robot self-localization

针对当出现一些未建模的机器人运动时(如碰撞或者绑架问题),以小采样数目实现常规Monte Carlo方法难以解决的问题,提出一种新的Monte Carlo定位算法,该算法同时采用p(Xkzk)与p(XkXk-1)作为重要性函数并从中进行采样,避免了采样集不包含真实位姿采样的情况,能够有效地解决全局定位与绑架问题.同时在重采样过程中引入了过收敛检验与均匀性检验用于判断采样与感知信息的匹配程度,以适时进行重采样,节省了计算资源并提高了定位效率.实验结果表明该方法具有良好的性能.

A novel Monte Carlo method is proposed aiming at the solution of unmodeled motion problem （such as bumping or kidnapped problem） which is inextricable merely using conventional Monte Carlo localization. By adopting both p（Xk｜zk） and p（Xk｜Xk-1 ） as importance functions and sampling from them, the global localization and kidnapped problems are figured out efficiently. The over-convergence and uniformity validations are introduced to verify correspondence between sample distribution and sensor information for timely resampling which highly saves computational resource and enhances localization efficiency. Experimental results validate the favorable performance of this approach.