## 1 Introduction  
With the rapid development of autonomous driving technology [1, 2, 3], open-pit mining environments as a closed scenario are suitable for autonomous truck operation. However, the environment of a mining area is characterized by heavy dust and bumpy roads, as shown in Figure 1, which sets high requirements for the implementation of autonomous driving. For autonomous trucks, one essential task is object detection, which is directly related to the safety and efficiency of mining operations. Object detection has attracted the interest of numerous scholars with different types of sensors explored (e.g., vision sensors and LiDAR) [1, 2, 4, 5]. Vision sensors are easily affected by undesirable light and weather conditions, and cannot acquire accurate depth information. By comparison, LiDAR, as a three-dimensional (3D) sensor, is advantageous due to not being affected by lighting and providing large amounts of depth information with high accuracy and long-range detection. Several LiDAR-based environment perception methods have been proposed to improve the performance of object detection for autonomous mining trucks, and encouraging results have been achieved [6, 7]. However,due to the harsh environmental conditions of open-pit mining areas, as shown in Figure 1a, there remain a series of challenges to be overcome. The first challenge is that road boundaries in a mining environment are not clear, hindering extracting the region of interest (ROI) to filter LiDAR points out of the road area, as shown in the red rectangles in Figure 1b. The second arises from the bumpiness of the road, leading to instability when detecting the ground according to the height information of the point clouds, as shown in Figure 1c. Furthermore, in view of the wide range of the size of different objects (e.g., pedestrians, vehicles, and trucks), over- or undersegmentation decreases clustering precision [8; 9]. To address the above problems, we propose a novel 3D LiDAR-based object detection method fusing semantic and geometric features for autonomous mining trucks in a mining environment (3 DSG) in this study. As shown in Figure 2, the pipeline of the proposed method mainly has three parts: road region extraction, ground detection, and object clustering. To reduce the computational burden, the semantic segmentation of point clouds is first performed to extract the ROI. Next, a cascaded ground detection method is employed to distinguish ground and nonground point clouds. Furthermore, a clustering algorithm adjusting Euclidean distance according to semantic categories was designed to classify the point clouds of different objects. The proposed strategy based on 3D LiDAR deployed in autonomous trucks is adaptive to object detection in mining environments with unstructured roads. The main contributions of this study are summarized as follows:  
* A cascaded ground detection algorithm based on semantic segmentation point filtering and rectangular grid map filtering was designed, which performs reliably in unstructured terrains such as a mining environment. Figure 1: Illustration of a mining environment. (**a**) Mining environment. (**b**) Road boundary. (**c**) Bumpy road. Figure 2: The pipeline of the proposed method. * To overcome over- and undersegmentation caused by the sparsity of point clouds, we propose a clustering method of adaptive Euclidean clustering distance thresholds according to semantic segmentation categories. * Semantic and geometric features are fused to enhance the object detection performance in terms of both efficiency and accuracy. The quantitative comparisons on the TG-Mine-3D dataset illustrate that our method achieved state-of-the-art performance on truck and pedestrian detection accuracy, and promising computational speed. The rest of this study is organized as follows. Section 2 presents a review of the related work. Section 3 details the proposed 3D LiDAR-based object detection method for autonomous mining trucks. Experimental results are shown in Section 4.