Linear Feature-Based Image/LiDAR Integration for a Stockpile Monitoring and Reporting Technology

Abstract

Stockpile monitoring has been recently conducted with the help of modern remote sensing techniquese.g., terrestrial/aerial photogrammetry/LiDARthat can efficiently produce accurate 3D models for the area of interest. However, monitoring of indoor stockpiles still requires more investigation due to unfavorable conditions in these environments such as lack of global navigation satellite system (GNSS) signals and/or homogenous texture. This study develops a fully-automated image/LiDAR integration framework that is capable of generating accurate 3D models with color information for stockpiles under challenging environmental conditions. The derived colorized 3D point cloud can be subsequently used for volume estimation and visual inspection of stockpiles. The main contribution of the developed strategy is using automatically derived conjugate image/LiDAR linear features for simultaneous registration and camera/LiDAR system calibration. Data for this study is acquired using a camera-assisted LiDAR mapping platformdenoted as stockpile monitoring and reporting technology (SMART) -which was recently designed as a time-efficient and cost-effective bulk material tracking. Experimental results on three datasets show that the developed framework outperforms a classical planar feature-based registration technique in terms of the alignment of acquired point cloud. Results also indicate that the proposed approach can lead to a high relative accuracy between image lines and their corresponding back-projected LiDAR features in the range of 4-7 pixels.

Comparative analysis of surface deformation monitoring in a mining area based on UAV‐lidar and UAV photogrammetry

“…The challenge persists in merging data from both techniques to enhance monitoring accuracy, particularly when the monitoring precision of both techniques is within the centimetre range (within 10 cm). Various methods can be employed for this purpose (Chu et al, 2023;Hasheminasab et al, 2023;Hu et al, 2022;Jonassen et al, 2023;Li et al, 2018;Lv & Ren, 2015), including weighted averaging, height information fusion, feature matching and registration, geometric precision correction. This study, however, focuses on the stochastic nature of monitoring data (Gaussian distribution), further understanding the differences in deformation scales between the two monitoring data types, providing additional possibilities for data fusion.…”

A Fast Registration Method for Building Point Clouds Obtained by Terrestrial Laser Scanner via 2-D Feature Points

“…The optimal transformation was obtained using a topological graph voting method. Hasheminasab et al [27] proposed a method to integrate image and LiDAR point cloud. In order to obtain the complete point clouds, the plane features were applied to carry out point cloud registration.…”

“…The plane/line-based methods [24]- [27] typically offer superior registration accuracy compared to the point-based methods because the line or plane features are higher-level attributes than the point features. The line or plane features are fitted by using many points, so they have higher accuracy than the point features.…”

Alternative Lidar Technologies for Stockpile Monitoring and Reporting

“…Using two LiDAR units facilitates covering a larger area while maintaining high-point cloud density. The RGB camera on these two platforms serves as a tool for the coarse alignment of the acquired LiDAR data (Hasheminasab et. al., 2023).…”