For the environmental perception of mobile robots with light detection and ranging(LIDAR), a method based on split-merge framework was presented herein. In splitting stage, a data set was segmented recursively with the IEPF(iterative end point fit) algorithm. In merging stage, both the EPF(end point fit) method and the total least square method were used to fit a new data set composed of the two adjacent segments. If their fitted errors were less than their thresholds respectively, the two data sets were merged into one. Merging stage was a recursive process for that it didn’t terminate until every two adjacent data sets didn’t satisfy the merging condition mentioned above. Results of comparative experiment show that the method presented reduces the probability of over-segmentation and under-segmentation resulted of the fixed threshold of the IEPF algorithm greatly and achieves good results of linear feature extraction.

mobile robot, linear feature extraction, light detection and ranging(LIDAR), split-merge frame

针对移动机器人依靠激光雷达感知环境的问题,提出一种基于分开-合并框架的直线特征提取算法。在分开阶段,用IEPF（iterative end point fit）算法对采集的激光雷达数据集合进行递归分割。在合并阶段,对由两相邻数据集合构成的新集合同时用EPF（end point fit）算法和总体最小二乘法进行拟合,如果两种拟合误差分别小于各自的阈值,则合并两个集合。合并阶段是一个递归过程,直到所有的两相邻数据集合都不满足上述合并条件才终止算法。对比实验结果表明,该算法大大降低了IEPF算法固定阈值所带来的过分割和欠分割的可能性,得到了很好的直线特征提取结果。

移动机器人, 直线特征提取, 激光雷达, 分开-合并框架