一种低计算量移动机器人自定位方法

移动机器人定位问题就是通过传感器数据来确定自己的位姿。本文介绍了几种基于概率的自定位算法。针对蒙特卡罗定位算法需要精确概率模型以及计算量大的问题，本文提出了一种均匀蒙特卡罗算法。该算法假设运动模型和感知模型都是均匀分布的，采样点在运动过程中不变，而且不需要精确的概率模型，计算量小，稳定性高。试验表朗，该算法能在室内环境下很好的对机器人定位。

Self-localization Algorithm of Mobile Robot with Lower Computational Cost

Mobile robot localization is the problem of determining a robot's pose relative to its environment from sensor data. This article presents several probabilistic localization algorithms. Uniform Monte Carlo Localization (UMCL) is required to solve lower computational cost and handling the unpredictable probability distribution models. UMCL uses only uniform distribution to represent probability distributions .It is robustness with lower computation cost. Experiment results show that UMCL is in good use of mobile robot localization.