Abstract: Monte Carlo Localization (MCL) algorithm has many problems such as large computation and poor positioning accuracy. Because of the diversity of information carried by two-dimensional code and usability and convenience of two-dimensional code recognition, an adaptive MCL algorithm integrated with two-dimensional code information was proposed. Firstly, the cumulative error of odometer model was corrected by absolute position information provided by two-dimensional code and then sampling was performed. Sencondly, the measurement model provided by laser sensor was used to determine the importance weights of the particles. Finally, as fixed sample set used in the resampling part caused large computation, Kullback-Leibler Distance (KLD) was utilized in resampling to reduce the computation by adaptively adjusting the number of particles required for the next iteration according to the distribution of particles in state space. Experimental result on the mobile robot show that the proposed algorithm improves the localization accuracy by 15.09% and reduces the localization time by 15.28% compared to traditional Monte-Carlo algorithm.

Key words: Monte-Carlo Localization (MCL), odometer motion model, measurement model, two-dimensional coding, Kullback-Leibler Distance (KLD) sampling

摘要： 蒙特卡洛定位（MCL）算法存在计算量大、定位精度差的问题，由于二维码具有携带信息的多样性、二维码识别的方便性与易用性的特点，提出一种融入二维码信息的自适应蒙特卡洛定位算法。首先，利用二维码提供的绝对位置信息修正里程计模型的累计误差后进行采样；然后，采用激光传感器提供的观测模型确定粒子的重要性权重；最后，因为重采样部分采用固定样本集会导致大计算量，所以利用Kullback-Leibler距离（KLD）进行重采样，根据粒子在状态空间的分布情况自适应调整下一次迭代所需粒子数，从而减小计算量。基于移动机器人进行的实验结果表明，改进算法与传统蒙特卡洛算法相比定位精度提高了15.09%，时间缩短了15.28%。

关键词: 蒙特卡洛定位, 里程计运动模型, 观测模型, 二维码, Kullback-Leibler距离采样