Indoor Stockpile Reconstruction Using Drone-Borne Actuated Single-Point LiDARs

Abstract

A low-cost, yet accurate approach for stockpile volume estimation within confined storage spaces is presented. The novel approach relies on actuating a single-point light detecting and ranging (1D LiDAR) sensor using a micro servo motor onboard a drone. The collected LiDAR ranges are converted to a point cloud that allows the reconstruction of 3D stockpiles, hence calculating the volume under the reconstructed surface. The proposed approach was assessed via simulations of a wide range of mission operating conditions while mapping two different stockpile shapes within the Webots robotic simulator. The influences from modulating the drone flight trajectory, servo motion waveform, flight speed, and yawing speed on the mapping performance were all investigated. For simple rectangular trajectories, it was found that having longer trajectories that are adjacent to the storage walls provides best reconstruction results with reasonable energy consumption. On the other hand, for short rectangular trajectories within the storage middle space, the yawing speed at corners must be decreased to ensure good reconstruction quality, although this can lead to relatively high energy consumption. Comparing the volumetric error values, the average error from the proposed 1D LiDAR system, when operating at 6°·s−1 maximum yawing speed at the corners, was 0.8 ± 1.1%, as opposed to 1.8 ± 1.7%, and 0.9 ± 1.0% from the 2D and 3D LiDAR options, respectively. Moreover, compared to 2D and 3D LiDARs, the proposed system requires less scanning speed for data acquisition, is much lighter, and allows a substantial reduction in cost.