Loader crane working area monitoring system based on lidar scanner

Abstract

Segmentation and analysis of the environment using 3D data (point clouds) in real time are dynamically developing the area. Falling prices of depth sensors based on technologies: LIDAR, ToF, RADAR, increasing computing power, growing interest in autonomous vehicles and robots, favor this trend. This paper presents test studies of loader crane working area monitoring system based on the Velodyne VLP-16 LIDAR scanner. Developed system use ground plane estimation and surroundings segmentation algorithms. The ground points filtering algorithm is based on the dot product of vectors as well as interpolation using the RANSAC method. Segmentation algorithm uses angle threshold between points and breadth-first search (BFS) method for determine neighborhood. The proposed system was adapted to operate with sparse LIDAR data in real time. Described system allows for detects human bodies, environmental elements, and monitors changes in the loader crane work area. The effectiveness of developed algorithms was tested on data obtained from loader crane test bench. An experiment showed that segmentation and monitoring loader crane working area in real time even with sparse data is possible. Moreover, the authors discuss other methods used to segmentation sparse point cloud in real time, describe the Velodyne VLP-16 scanner, and presents an outline of current research into HMI interfaces for loader cranes.