| 黄军芬,王贵龙,薛龙,曹莹瑜,刘学城.基于激光雷达的爬楼机器人楼梯检测与定位[J].电子测量与仪器学报,2026,40(6):179-188 |
| 基于激光雷达的爬楼机器人楼梯检测与定位 |
| LiDAR-based stair detection and localization for stair-climbing robots |
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| DOI： |
| 中文关键词: 爬楼机器人 楼梯检测 旋转式激光雷达 点云处理 空间定位 |
| 英文关键词:stair-climbing robot stair detection rotating LiDAR point cloud processing spatial localization |
| 基金项目:国家重点研发计划项目(2023YFB4707200)资助 |
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| Author | Institution |
| Huang Junfen | Beijing Institute of Petrochemical Technology, Beijing 102617, China |
| Wang Guilong | Beijing Institute of Petrochemical Technology, Beijing 102617, China |
| Xue Long | Beijing Institute of Petrochemical Technology, Beijing 102617, China |
| Cao Yingyu | Beijing Institute of Petrochemical Technology, Beijing 102617, China |
| Liu Xuecheng | Beijing Institute of Petrochemical Technology, Beijing 102617, China |
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| 中文摘要: |
| 为提升爬楼机器人在楼宇内环境中对楼梯的快速检测与精确定位能力，提出了一种基于旋转式2D激光雷达的楼梯检测与定位方法。搭建了由舵机、2D激光雷达、上位机树莓派（Raspberry Pi 5）和下位机大疆A型开发板构成的硬件平台。在此基础上，针对机器人与楼梯处于不同相对位置关系时的楼梯检测场景，提出了基于点云区域生长聚类与主成分分析的楼梯多层台阶立面特征检测算法,并计算多层台阶上行方向角度,确定多场景下楼梯上行方向与机器人X轴向在X-Y平面上的夹角;以楼梯首级台阶立面中心点为关键定位参数，基于主成分分析的多层台阶立面拟合算法获取台阶高度H、长度L及首层台阶立面中心点坐标等关键定位参数,实现对楼梯的精确定位。开展了4类楼梯场景下机器人相对楼梯处于不同位置关系的240组实验，得出楼梯上行方向角度检测偏差绝对值整体处于［0.03°，2.35°］的区间内,平均偏差控制在1.5°以内;通过不同楼梯场景下的楼梯台阶定位实验，得出机器人坐标系下楼梯首层台阶立面中心点的X方向最大定位偏差为0.024 m,Y方向最大定位偏差为0.062 m。实验结果表明,该方法在多种楼梯场景下均具备较高的检测准确率及定位精度,可为爬楼机器人在复杂楼宇内环境中的自主跨层作业提供可靠的感知与定位支持。 |
| 英文摘要: |
| In order to enhance the stair-climbing robot’s capability for rapid detection and precise localization of stairs in indoor environments, a stair detection and localization method based on a rotating 2D LiDAR was proposed. A hardware platform consisting of a servo motor, a 2D LiDAR, a Raspberry Pi upper computer, and a DJI Type A developer board was constructed. On this basis, for various relative positional relationships between the robot and the stairs, a stair detection algorithm based on point cloud region-growing clustering and principal component analysis (PCA) was proposed to extract multi-level step facade features and compute the ascent direction angle of the stairs. This enabled the determination of the angle between the stair ascent direction and the robot’s X-axis in the X-Y plane across multiple scenarios. Using the center point of the first-step facade as a key localization parameter, a PCA-based plane fitting algorithm was employed to obtain critical parameters such as step height (H), length (L), and the coordinates of the first-step facade center point, thereby achieving precise stair localization. A total of 240 experiments were conducted across four types of stair scenarios with varying robot-stair relative positions. The results showed that the absolute deviation in stair ascent direction detection ranged from 0.03° to 2.35°, with an average deviation within 1.5°. Localization experiments in different stair scenarios revealed that the maximum positioning deviation of the first-step facade center point in the robot coordinate system was 0.024 m in the X-direction and 0.062 m in the Y-direction. The experimental results demonstrate that the proposed method achieves high detection accuracy and positioning precision across various stair scenarios, providing reliable perception and localization support for autonomous cross-floor navigation of stair-climbing robots in complex indoor environments. |
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