Resumen
Object detection is an essential task for autonomous vehicles to ensure safety. Due to the complexity of mining environments, it is difficult to detect objects accurately and robustly. To address these issues, this paper proposes a novel 3D LiDAR-based object detection method fusing semantic and geometric features for autonomous trucks in mining environments (3DSG). A road region extraction method is presented by establishing a semantic segmentation network with a region searching strategy to eliminate off-road point clouds. To deal with the complexity of unstructured road ground point-cloud segmentation, we propose a cascaded ground detection algorithm by performing semantic segmentation filtering and rectangular grid map filtering. A clustering method is proposed fusing adaptive distance thresholds of Euclidean clusters with semantic segmentation categories to solve the problem of the over- and undersegmentation of objects caused by the sparsity of point clouds. The performance of the proposed method is examined utilizing a real mining dataset named TG-Mine-3D. Compared with state-of-the-art methods, our method achieved higher precision of 66.39%. Moreover, for the truck and pedestrian categories, the performance of our method was significantly improved by 2.66% and 5.80%, respectively. The proposed method running at 51.35 ms achieved real-time performance.