Leg Detection for Socially Assistive Robots: Differentiating Multiple Targets with 2D LiDAR

While socially assistive robots working in environments with a lot of people walking and obstructions, LiDAR-based detectors may have trouble locating the target person. The lack of identifying information is a drawback of the 2D range data obtained by a LiDAR sensor. Consequently, when applying the traditional technique of clustering 2D laser dots with geometric properties, modern LiDAR-based leg detectors typically fail. To recognize and identify the target individual in real time, an improved leg detector based on density-weighted support vector data description (DW-SVDD) is presented. The suggested DW-SVDD leg detector in this study incorporates density weight, width, and girth features into the support vector data description. Socially assistive robots that follow humans may quickly and accurately identify items from vast quantities of 2D laser point data with this detector. To evaluate detection accuracy, the proposed leg detector is tested on a mobile robot both indoors and outdoors. Field experiment results show that the proposed DW-SVDD leg detector enables socially assistive robots to detect partial occlusions and similar obstacles effectively. Additionally, the results indicate that the proposed leg detector achieves an MOTA of 34.96% and MOTP of 39.17%, demonstrating its efficacy in practical applications.