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 techniques – e.g., terrestrial/aerial photogrammetry/LiDAR – that 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 platform – denoted 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.