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中文摘要：
为解决大规模环境下机器人的同时定位和地图构建（SLAM）问题，提出一种基于Rao—Blackwellised粒子滤波器的SLAM算法．通过选取稳定且易于区别的特征点，发展了一种基于全局约束的数据关联方法，有效地减少了误匹配的概率；采用改进的粒子分布预测函数，提高了粒子滤波器的性能．实验结果表明，该算法具有较低的计算复杂度，精度也比较高，能够有效地解决大规模环境下的机器人SLAM问题．
英文摘要：
A simultaneous localization and mapping （SLAM） algorithm based on Rao-Blackwellised particle filter was presented for autonomous robot in large scale environment. Through selecting the stable and distinctive landmarks, a new data association method was proposed based on global constraints, accordingly the probability of error matching was decreased much. Then, an improved proposal function was adopted to improve the performance of particle filter. Experimental results showed that the algorithm has low computational complexity and high precision, and it can solve the robot SLAM problem in large scale environment.
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