In transportation at open-pit mines, rocks dropped as a mining truck is driven will wear out the tires of the vehicle, thus increasing the mining cost. In the case of autonomous vehicles, the vehicle must automatically detect rocks on the transportation roads during the driving process. This will be a new challenge: rough road, rocks of small size and irregular shape, long detection distance, etc. This paper presents a detection method based on light detection and ranging (lidar). It includes two stages: (1) using the modified cloth simulation method to filter out the ground points; (2) using the regional growth method based on grid division to cluster non-ground points. Experimental results show that the method can detect rocks with a size of 20–30 cm at a distance of 40 m in front of the vehicle, and it takes only 0.3 s on an ordinary personal computer (PC). This method is easy to understand, and it has fewer parameters to be adjusted. Therefore, it is a better method for detecting small, irregular obstacles on a low-speed, unstructured and rough road.
The effect of geological modeling largely depends on the normal estimation results of geological sampling points. However, due to the sparse and uneven characteristics of geological sampling points, the results of normal estimation have great uncertainty. This paper proposes a geological modeling method based on the dynamic normal estimation of sparse point clouds. The improved method consists of three stages: (1) using an improved local plane fitting method to estimate the normals of the point clouds; (2) using an improved minimum spanning tree method to redirect the normals of the point clouds; (3) using an implicit function to construct a geological model. The innovation of this method is an iterative estimation of the point cloud normal. The geological engineer adjusts the normal direction of some point clouds according to the geological law, and then the method uses these correct point cloud normals as a reference to estimate the normals of all point clouds. By continuously repeating the iterative process, the normal estimation result will be more accurate. Experimental results show that compared with the original method, the improved method is more suitable for the normal estimation of sparse point clouds by adjusting normals, according to prior knowledge, dynamically.
In this paper, we propose a novel mesh repairing method for repairing voids from several meshes to ensure a desired topological correctness. The input to our method is several closed and manifold meshes without labels. The basic idea of the method is to search for and repair voids based on a multi-labeled mesh data structure and the idea of graph theory. We propose the judgment rules of voids between the input meshes and the method of void repairing based on the specified model priorities. It consists of three steps: (a) converting the input meshes into a multi-labeled mesh; (b) searching for quasi-voids using the breadth-first searching algorithm and determining true voids via the judgment rules of voids; (c) repairing voids by modifying mesh labels. The method can repair the voids accurately and only few invalid triangular facets are removed. In general, the method can repair meshes with one hundred thousand facets in approximately one second on very modest hardware. Moreover, it can be easily extended to process large-scale polygon models with millions of polygons. The experimental results of several data sets show the reliability and performance of the void repairing method based on the multi-labeled triangular mesh.
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