Kitamoto Sora, Hiroi Yutaka, Miyawaki Kenzaburo, Ito Akinori
Graduate School of Robotics and Design, Osaka Institute of Technology, Osaka 530-8568, Japan.
Faculty of Robotics and Design, Osaka Institute of Technology, Osaka 530-8568, Japan.
Sensors (Basel). 2025 Mar 12;25(6):1754. doi: 10.3390/s25061754.
Human tracking is a fundamental technology for mobile robots that work with humans. Various devices are used to observe humans, such as cameras, RGB-D sensors, millimeter-wave radars, and laser range finders (LRF). Typical LRF measurements observe only the surroundings on a particular horizontal plane. Human recognition using an LRF has a low computational load and is suitable for mobile robots. However, it is vulnerable to variations in human height, potentially leading to detection failures for individuals taller or shorter than the standard height. This work aims to develop a method that is robust to height differences among humans using a 3D LiDAR. We observed the environment using a 3D LiDAR and projected the point cloud onto a single horizontal plane to apply a human-tracking method for 2D LRFs. We investigated the optimal height range of the point clouds for projection and found that using 30% of the point clouds from the top of the measured person provided the most stable tracking. The results of the path-following experiments revealed that the proposed method reduced the proportion of outlier points compared to projecting all the points (from 3.63% to 1.75%). As a result, the proposed method was effective in achieving robust human following.
人体跟踪是与人类协作的移动机器人的一项基础技术。人们使用各种设备来观察人体，如摄像头、RGB-D传感器、毫米波雷达和激光测距仪（LRF）。典型的LRF测量仅能观察特定水平面上的周围环境。使用LRF进行人体识别计算量小，适用于移动机器人。然而，它容易受到人体身高变化的影响，可能导致高于或低于标准身高的个体检测失败。这项工作旨在开发一种使用三维激光雷达对人体身高差异具有鲁棒性的方法。我们使用三维激光雷达观察环境，并将点云投影到单个水平面上，以应用二维LRF的人体跟踪方法。我们研究了用于投影的点云的最佳高度范围，发现使用被测人员顶部30%的点云可提供最稳定的跟踪。路径跟踪实验结果表明，与投影所有点相比，该方法减少了异常点的比例（从3.63%降至1.75%）。因此，该方法在实现稳健的人体跟踪方面是有效的。
