## 1 Introduction  
Cost control has always been the top priority in the mining industry, and how to control the cost is one of the critical points for mining companies to make profits. In the open-pit mines, the transportation cost accounts for about 50% of the total mining cost [1; 2; 3]. In the absence of mining technology innovation, how to improve transportation efficiency and reduce extra transportation costs are crucial to improve the profits of mining enterprises. The rise of driverless technology makes the problem have a new solution. At present, unmanned ground vehicles (UGVs) have been widely used in port or warehouse automation logistics, disaster relief, military, and other industrial and military fields [4; 5; 6; 7; 8]. In the mining field, autonomous navigation of the load-haul-dump (LHD) machine in underground mines has been successfully applied [9; 10]. However, it is still a challenge to apply autonomous technology in open-pit mining. The open-pit mining area is an ideal environment for unmanned vehicles due to its closed environment and few external interference factors. Komatsu and other mining equipment manufacturing giants have carried out unmanned transportation experiments in a Chilean copper mine since 2008 [11]. Their results show that the transportation capacity is increased by 40% and the cost has reduced by 15% in the autonomous haulage system (AHS). Another unmanned transportation experiment in Australian mining also shows that unmanned transportation has more considerable economic significance compared with traditional transportation [12]. Nonetheless, [PERSON] [13] and [PERSON] [14] both pointed out that the AHS may have problems such as unable to respond to ground pits or rocks promptly, resulting in extra fuel consumption and tire wear. Also, some studies [3,15] have concluded that, when haulage trucks run on poor road conditions, the vehicle cost will be increased. [PERSON] [16], through the simulation experiment, concluded that under the general mining road conditions, the dynamic load of the tire would be 60% higher than the static load, which will aggravate the tire wear. For the existing AHS, only the large obstacles on the ground are considered in path planning. In contrast, the tiny obstacles are ignored, like rocks or terrain undulation on the surface, resulting in additional tire wear. Thus, the truck route should be planned on smoother ground. The path planning of UGV is divided into route planning, trajectory planning, and motion planning [17]. Route planning is point-to-point planning, which regards the vehicle as a particle and does not consider other constraints, so it requires less map accuracy. Trajectory planning is based on route planning, with more constraints of vehicles, such Cering the most constraints, such as vehicle control strategy. [PERSON] et al. [18] used the map from Google Earth to optimize the route of dump trucks in open-pit mines. Google Earth only provides a low-resolution surface elevation model, but the realization of trajectory planning requires more accurate surface topographic maps. The digital surface model (DSM) is a model that contains the surface elevation information, and it is generally used for the reconstruction of the terrain model as its high precision. There are several methods to build a DSM: traditional digital mapping [19], unmanned aerial vehicle (UAV) lidar mapping [20,21], oblique photogrammetry [22], etc. Among these methods, the oblique photogrammetry has been widely used in urban surveying and mapping [23], disaster monitoring [24], and the open-pit mining industry [25, 26, 27] because of its high precision and convenient operation. Based on DSM, trajectory planning can be carried out. [PERSON] et al. [28] planned a coverage path for UGV of vineyard based on DSM. [PERSON] et al. [29] used lidar to measure the surface elevation point cloud and constructed the ground map to carry out the path planning of cross-country vehicles.