## 2 Related Work
### Research on Traditional Methods  
Traditional methods perform object detection according to the geometric features of point clouds with certain assumptions, such as the road surface being fitted into one or multiple planes, and the distinct height between obstacles and the ground. Three basic steps are generally involved: ground and nonground cloud segmentation, nonground cloud clustering, and bounding box fitting. Though they have high computational efficiency, these methods usually have a large number of parameters to be calibrated and validated, resulting in undesirable robustness.  
(1) Ground segmentation: Various types of ground segmentation methods have been proposed. They can be roughly divided into six categories [10]; the most widely used are plane-fitting and elevation-map methods. Specifically, traditional methods are more applicable to object detection in urban environments with a flat ground and clear road structures. For example, the ground surface was estimated by fitting planes on the basis of the RANSAC approach to a single or multiregion plane [10; 11]. The ground and nonground points were separated on the basis of their distance from the plane. However, the plane-fitting method assumes that the ground is flat or has a gradual slope. Roads in a mining environment have rugged characteristics and cannot be represented in planes. Thus, the plane-fitting method is not suitable for mining environments. In the literature [12; 13], LiDAR points were projected onto a grid map to classify nonground clouds. For each cell of the grid map, feature values were calculated on the basis of the average height, height variance, or maximal and minimal height differences. The grid-map method has high computational efficiency because it projects raw point clouds onto a fixed-size map, which reduces the amount of processed data. However, due to the principal characteristics of grid map methods, the ground under overhanging objects such as bridges is detected as a nonground point cloud [10]. Moreover, the sloped terrain in mining environments can be problematic for grid-map methods, as the feature values are similar to the nonground point clouds.  
(2) Object Clustering: Scholars have proposed many point-cloud clustering methods, roughly divided into several categories, such as distance-based Euclidean distance clustering [8; 14; 15] and density-based DBSCAN [16; 17]. In [14], a Euclidean-based clustering method was proposed with a radially bounded nearest-neighbor strategy (RBNN). The authors further extended RBNN by considering the normal vector, Euclidean, and angular distance to previously clustered points [15], which rendered the algorithm prone to segmenting surfaces. In [8], the authors proposed an adaptive distance threshold Euclidean clustering method to solve the problems of over- and undersegmentation caused by the fixed parameters. However, clustering methods using only the spatial distance between points cannot address the problem of oversegmentation when an object is occluded, and undersegmentation when multiple objects are close to each other. The density-based clustering method is suitable for arbitrary shapes and is robust to noise. DSets-DBSCAN [18] is an extension to DBSCAN [16] to achieve parameter-free clustering. In [17], the authors proposed a modified version of the DBSCAN method that improved clustering stability for the case of objects being close to each other. However, the density-based clustering method was not effective for point clouds with uneven density.