电子测量与仪器学报

叶一飞,王建中.基于点云的复杂环境下楼梯区域识别[J].电子测量与仪器学报,2020,34(4):124-133

基于点云的复杂环境下楼梯区域识别

Stair area recognition in complex environment based on point cloud

DOI：

英文关键词:complex environment stair detection point cloud region segmentation

基金项目:国防基础科研计划(JCKY2017602C016)资助项目

作者	单位
叶一飞	1.北京理工大学机电学院
王建中	1.北京理工大学机电学院

Author	Institution
Ye Yifei	1.School of Mechatronical Engineering, Beijing Institute of Technology
Wang Jianzhong	1.School of Mechatronical Engineering, Beijing Institute of Technology

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中文摘要:

自主移动机器人已经在国防军事、灾区救援等领域有了广泛应用,楼梯区域作为一种典型的环境目标,需要机器人能够对其做出准确识别。放置于楼梯区域的障碍物会破坏传统楼梯识别算法需要提取的楼梯平面及边缘特征,导致楼梯区域无法被准确识别。针对该问题,提出了一种复杂环境下,基于点云的楼梯区域检测识别算法。该算法首先使用区域生长方法对目标区域进行分割并通过各区域拟合平面法线方向来选取疑似为楼梯各级竖直阶面的区域;进而对各级楼梯区域进行处理,分割障碍物并获取各级楼梯竖直面上边界;最终根据各级边界位置得到楼梯模型以及障碍物位置。实验结果表明,该算法具有较好的鲁棒性,能在各种复杂环境下识别出楼梯并得到无障碍楼梯区域,构建的楼梯三维模型尺寸误差均小于 7%,有较高的准确性, 相较于传统楼梯识别算法,能达到更好的检测识别效果。

英文摘要:

Autonomous mobile robots have been widely used in national defense, disaster relief and other fields. As a typical environmental target, the stair area needs to be accurately recognized by the robot. Obstacles placed in the stair area will destroy the stairs’ plane and edge features that traditional staircase recognition algorithms need to extract, resulting in the staircase area cannot be accurately recognized. Aiming at this problem, a point cloud-based stair area detection and recognition algorithm in a complex environment is proposed. The algorithm first uses the region growing method to segment the target region and selects the regions suspected to be the vertical step of each level of the stair by fitting the plane normal direction of each region, then processes the each level stair area to segment obstacles and obtain the boundaries on the vertical plane of each level of the stair. Finally, the stair model and obstacle position are obtained according to the boundary position of each level. The experimental results show that the algorithm has better robustness, can recognize stairs in various complex environments and get barrier-free stair area. The constructed 3D model of stairs has a size error of less than 7%, which is higher accuracy. The algorithm can achieve better detection and recognition results compared with traditional stairs recognition algorithms.

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