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.

A Novel Multi-LiDAR-Based Point Cloud Stitching Method Based on a Constrained Particle Filter

“…The algorithm assumes that the rotation plane remains constant. The formula can be expressed as follows: (9) where P i represents the original point cloud, P m denotes the point cloud after undergoing particle filtering, G V signifies voxel filtering, G S indicates statistical filtering, and MLS represents the moving least square.…”

“…Drone scanning involves the use of unmanned aerial vehicles (UAVs) equipped with cameras or laser scanners to perform aerial scans of coal piles [8][9][10][11]. This system can rapidly capture surface data of extensive coal piles within a short timeframe, thereby offering the advantages of improved resolution and comprehensiveness.…”

“…However, drone scanning does have certain limitations. A study by Alsayed et al [9] revealed that drones equipped with LiDAR sensors may have blind spots during data scanning, particularly in enclosed coal yard environments, which pose challenges for optimizing flight trajectories and may require further refinement. Additionally, research by Davis et al [12] demonstrated that increasing the altitude of drone flights can result in greater errors and reduced reliability.…”

Stockpile Volume Estimation in Open and Confined Environments: A Review

“…Alsayed and Nabawy [101] proposed a simple and cost-effective solution for stockpile scanning that relied on a single-point LiDAR (1D LiDAR) sensor actuated by a micro servo motor attached to a drone. This method was evaluated through simulations to understand the impact of the various operating conditions on mapping performance.…”