Current methods for estimating the size of a salt pile or other bulk material in 3D piles include using photographic imaging, total stations, tape measures and/or static laser scanning. These methods can be time- and labor-intensive, expensive, dangerous and inaccurate.
Researchers at Purdue University have developed a new method to determine the amount of bulk material in a 3D pile (e.g., salt piles at Department of Transportation storage facilities). The technology and method developed by the Purdue researchers involves using LiDAR (light detection and ranging) to create a 3D point cloud and using it to estimate the volume of a 3D pile. This technology is portable, compact, adaptable and simple.
IN THE MEDIA
Purdue University News Release
Ayman Habib, the Thomas A. Page Professor of Civil Engineering, Lyles School of Civil Engineering, College of Engineering
Stockpile Monitoring and Reporting Technology (SMART)
Image-Aided LiDAR Mapping Platform and Data Processing Strategy for Stockpile Volume Estimation
Raja Manish, Seyyed Meghdad Hasheminasab, Jidong Liu, Yerassyl Koshan, Justin Anthony Mahlberg, Yi-Chun Lin, Radhika Ravi, Tian Zhou, Jeremy McGuffey, Timothy Wells, Darcy Bullock and Ayman Habib (Project Leader)
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.