## 4 Autonomous Shovel Technology
### Rock Pile Identification and Modeling Technology
#### 4.1.2 Rock Pile Identification Based on Distance Sensor  
The distance sensor mainly obtains the size information of the object by detecting the time interval from the light pulse emission to object reflection [42].  
Carnegie Mellon University developed the first large-scale excavation and loading automatic system (Autonomous Loading System, ALS), which uses laser scanners to scan the to-be-excavated area for planning of the optimal excavation location. However, this system is mainly favorable for the excavation of soft-soil ore piles; no research has been carried out on the excavation of large hard ore piles [43].  
The particles in the ore pile are always irregularly shaped without uniform sizes, since they are usually obtained after blasting the mine. Therefore, in order to avoid shovel loading failure or damage to the scraper, pretreatment must be performed first, meaning large ore blocks in the ore pile should be crushed or removed if they exist [44; 45; 46]. [PERSON] et al. generated point cloud images with a rock pile recognition algorithm based on the images from the time-of-flight camera successfully. This technology realized good recognition  
Figure 4: Construction of 3D model of ore pile. (**a**) The original map of the ore heap; (**b**) 3D model diagram [41].  
of ore piles without the help of other sensors, which is beneficial for the extraction of bulk ores from irregular heap surfaces, as demonstrated in Figure 5. It can be seen that point cloud images could be generated based on the original photo by the camera, as expected. Moreover, the sampled lump ores, numbered as 1 to 4 in the point cloud image (Figure 5b), could be recognized clearly and matched quite well with those in the original one (Figure 5a) [47].  
The advantage of mine pile identification based on distance sensors is that it can completely collect the mine environment information and create a 3D mine pile surface model quickly. However, only the surface information on the ore pile can be acquired through the distance sensor and the texture information of the surface is easy to lose [5]. Moreover, the modeling speed will be slowed down for high-density data processing, which is a result of ore pile scanning from multiple perspectives.  
To sum up, a single type of mine pile identification based on either image sensors or distance sensors has its drawbacks. Therefore, the cooperation of both sensors on ore pile identification and modeling technology would be a considerable research direction for future underground intelligent LHDs. As the example, shown in Figure 6, both types of sensors are adopted as two sets of laser scanner and camera (one in the front and the other in the back) on the LHD to collect information of the ore piles. After data recognition and integration, the 3D heap model for even dark and dusty underground environments can be quickly established. The model can also be updated in real time with changes in the field conditions [48].  
Figure 5: Point cloud processing of mine pile. (**a**) Mine pile original image; (**b**) mine pile point cloud image [47].  
Figure 6: Schematic diagram for the installation location of the laser scanner and camera on the underground scraper [49].