To address the challenges faced in the real-time detection of small-size rockfalls in open-pit mines during the transportation of ores using unmanned carts owing to suboptimal road conditions,intense lighting,and heavy dust,this study proposes a method for detecting small-size rockfalls in open-pit mines based on solid-state lidar.The proposed method employed a double-echo lidar for data acquisition,effectively reducing dust interference and extracting the driving area in front of the vehicle.Subsequently,a ground segmentation algorithm(straight-line fitting)based on fan surfaces was employed to segment the rough and unstructured terrains having slopes.Moreover,a hierarchical grid tree model known as octree was introduced to enhance the efficiency of neighborhood search.Furthermore,the two-color nearest pair method was applied to construct a graph,rapidly generating the clusters.Finally,the concept of adaptive clustering radius ε was adopted for clustering and obtaining the box models of small-size rockfalls.The experimental results demonstrate that the proposed method outperforms the k-d tree-accelerated DBSCAN algorithm,increasing the positive detection rate by 9.61 percentage points and reducing the detection time by 379.77 ms.
open-pit minesunmanned drivingobstacle detectionlidar point clouddensity clustering
NETL
NSTL
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