Fast Detection of Idler Supports Using Density Histograms in Belt Conveyor Inspection with a Mobile Robot

Abstract

The automatic inspection of belt conveyors gathers increasing attention in the mining industry. The utilization of mobile robots to perform the inspection allows increasing the frequency and precision of inspection data collection. One of the issues that needs to be solved is the location of inspected objects, such as, for example, conveyor idlers in the vicinity of the robot. This paper presents a novel approach to analyze the 3D LIDAR data to detect idler frames in real time with high accuracy. Our method processes a point cloud image to determine positions of the frames relative to the robot. The detection algorithm utilizes density histograms, Euclidean clustering, and a dimension-based classifier. The proposed data flow focuses on separate processing of single scans independently, to minimize the computational load, necessary for real-time performance. The algorithm is verified with data recorded in a raw material processing plant by comparing the results with human-labeled objects. The proposed process is capable of detecting idler frames in a single 3D scan with accuracy above 83%. The average processing time of a single scan is under 22 ms, with a maximum of 75 ms, ensuring that idler frames are detected within the scan acquisition period, allowing continuous operation without delays. These results demonstrate that the algorithm enables the fast and accurate detection and localization of idler frames in real-world scenarios.

Keywords

• density histogram
• RANSAC
• Euclidean clustering