# Autonomous Loading System for Load-Haul-Dump (LHD) Machines Used in Underground Mining
## 3 Proposed Autonomous Loading System
### Environment Modeling and Rock Pile Identification  
Of the six steps of the autonomous loading routine, the first one, rock pile identification, is the one that most strongly relies on the processing of data acquired from the environment. The aim of this step is to find the location and status of the draw point accurately, in real time, and without the need of extra steps or delays. This means that the LHD is required to continue moving while scanning the pile. The characterization of the rock pile is performed through integration of consecutive laser scans from the frontal 2D LIDAR sensor mounted on the LHD. The integration of the laser scans relies on a LIDAR-based odometry method [31], and the output of the machine's inclinometer data, which is filtered and integrated in the local coordinate frame in order to benefit from the natural tilting of the LHD while moving across uneven terrain. The aforementioned odometry method consists of an adaptive frame rate modification of the range-flow based odometry (RF2O) [32]. While RF2O computes displacement by comparing two scans, using the \"range flow constraint equation\", the adaptive frame rate implements a control over the processing frequency, increasing it at higher machine speeds. A block diagram for the pile identification step is presented in Figure 7. The LIDAR odometry enables the integration of 2D laser scans into a 3D point cloud of the environment. Then, the rock pile is segmented, and its features are computed (center, limits, and slope). A conceptual diagram of the complete pile identification process is shown in Figure 8. The succession of images (a, b, and c) illustrates how the integration is carried out. For each new scan (colored circles), the data is registered in an accumulated point cloud, using the computed odometry. It is important to note that the whole process must be executed in  
Figure 6: Finite state machine formulation for the autonomous loading with human operators in-the-loop. Figure 7: Rock pile identification block diagram. LIDAR odometry and machine’s inclinometer data are used to register 2D laser scans into a 3D model of the environment. A segmentation of the rock pile is then carried out, and its center, limits, and slope are extracted. real time without stopping, or even slowing down, the LHD. Moreover, the fact that the LIDAR reaches the bottom of the pile from a long distance away, because it is tilted down 2.5 degrees, results in the point cloud having very low density, all of which increases the complexity of the problem. Once enough scans have been integrated, the rock pile is segmented using the normal vectors of the surface, a curvature filter, and neighbors count in a small radius. The normal vectors provide information about the points belonging to the walls or to the pile (normal _my_ is negative on the left wall, near zero on the pile, and positive on the right wall). The curvature provides a rotation-invariant description of the points, which can be used for further segmenting the pile. Finally, neighbor counts provide information valuable for rejecting outliers (points that are isolated from all others). Once a pile is segmented successfully, its mean slope is estimated and is used to classify the status of the pile: a too shallow slope indicates there is not enough ore to perform a loading, and a too steep slope signals the presence of a tunnel wall instead of a rock pile, or even a rock pile in a \"hang-up\" state, a condition that can arise in certain underground mining methods, such as block caving, in which the fragmented rock stops flowing, generating an unstable and hazardous condition. The determination of the status of the pile is performed by a simple thresholding of its slope. For clarity purposes, the described algorithm for segmenting and characterizing the pile is detailed in Figure 9.  
Figure 8: Rock pile reconstruction process through laser-scan integration. As the machine approaches the pile (images (**a–c**)), the new laser scans are merged with the previous ones using the computed odometry.