Identification of intrusion obstacles for underground locomotives based on the fusion of LiDAR and wireless positioning technology

Relying solely on a single camera or radar for obstacle identification is limited under the complex underground mine environment. Given the computational limitations of the hardware and the specific adaptation requirements for underground environments, this paper establishes an experimental platform for underground locomotive intrusion obstacle identification. The coordinate systems of different LiDAR are firstly unified. The height threshold method is improved to perform ground segmentation on the raw data. The Euclidean clustering algorithm is improved with the lidar Based on Scan Line Distribution (BSLD) feature to obtain information such as the outline dimensions and central position of obstacle targets. Ultra wide band (UWB) positioning devices are combined with recorded map information to delineate the region of interest, identify and extract intrusion obstacles within these areas. The experimental results show that the system can reliably identify intrusion obstacles, significantly enhancing the pertinency of the obstacle identification system.