Image-Aided LiDAR Mapping Platform and Data Processing Strategy for Stockpile Volume Estimation

Abstract

Stockpile quantity monitoring is vital for agencies and businesses to maintain inventory of bulk material such as salt, sand, aggregate, lime, and many other materials commonly used in agriculture, highways, and industrial applications. Traditional approaches for volumetric assessment of bulk material stockpiles, e.g., truckload counting, are inaccurate and prone to cumulative errors over long time. Modern aerial and terrestrial remote sensing platforms equipped with camera and/or light detection and ranging (LiDAR) units have been increasingly popular for conducting high-fidelity geometric measurements. Current use of these sensing technologies for stockpile volume estimation is impacted by environmental conditions such as lack of global navigation satellite system (GNSS) signals, poor lighting, and/or featureless surfaces. This study addresses these limitations through a new mapping platform denoted as Stockpile Monitoring and Reporting Technology (SMART), which is designed and integrated as a time-efficient, cost-effective stockpile monitoring solution. The novel mapping framework is realized through camera and LiDAR data-fusion that facilitates stockpile volume estimation in challenging environmental conditions. LiDAR point clouds are derived through a sequence of data collections from different scans. In order to handle the sparse nature of the collected data at a given scan, an automated image-aided LiDAR coarse registration technique is developed followed by a new segmentation approach to derive features, which are used for fine registration. The resulting 3D point cloud is subsequently used for accurate volume estimation. Field surveys were conducted on stockpiles of varying size and shape complexity. Independent assessment of stockpile volume using terrestrial laser scanners (TLS) shows that the developed framework had close to 1% relative error.

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

“…Two LiDAR sensors with cross orientation are used to increase the area covered by the SMART system from a given data acquisition instance. More details regarding the 3 > REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) < SMART system design and specifications can be found in [5]. More details regarding the SMART system design and specifications can be found in [5].…”

“…In order to overcome the limitations of current stockpile monitoring technologies, a camera-assisted LiDAR mapping platform denoted as stockpile monitoring and reporting technology (SMART) was designed and introduced in our previous work [5]. Different from system-driven technologies such as TLS that use sophisticated and expensive encoders, the SMART system focuses on a data-driven strategy through acquiring data in a simple, cost-effective procedure.…”

“…However, the performance of such algorithms significantly degrades when dealing with images with homogenous texture, short baseline, and/or large changes in their viewpoints. It is important to note that these characteristics are dominant for data acquired by the SMART system [5]; thus, current approaches are not applicable for the involved camera/LiDAR integration in this study.…”

Automatic Extrinsic Calibration of 3D LIDAR and Multi-Cameras Based on Graph Optimization

“…In recent years, with the increasing demands on perception performance in the fields of mobile robots [ 1 , 2 , 3 ], surveying and mapping [ 4 , 5 , 6 , 7 , 8 ], 3D reconstruction [ 9 , 10 , 11 , 12 ], and autonomous driving [ 13 , 14 ], the application of multi-sensor fusion technology is more and more extensive [ 15 , 16 , 17 ]. Among them, LIDAR and cameras perform particularly well in multi-sensor fusion technology [ 18 , 19 , 20 , 21 ].…”

Calibration and Confidence in Snowplow Fleet Operations and Fleet Telematics

“…Additionally, INDOT operates 33 Road Weather Information Systems (RWIS) that help it better anticipate an impending winter storm event, pre-treat roadways well in advance of the first precipitation event and thus help save on material costs [7]. INDOT also has been utilizing LiDAR-based mobile/portable infrastructure to accurately monitor salt usage during the winter storm season to better estimate current and future material demand [8].…”