LAUSR

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 3-D models for the area of interest. However, monitoring of indoor stockpiles still requires more investigation due to unfavorable conditions in these environments such as a lack of global navigation satellite system signals and/or homogenous texture. This article develops a fully automated image/LiDAR integration framework that is capable of generating accurate 3-D models with color information for stockpiles under challenging environmental conditions. The derived colorized 3-D 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 article are acquired using a camera-assisted LiDAR mapping platform—denoted as stockpile monitoring and reporting technology—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.