Research on estimating the shape and volume of materials dumped by dump trucks using deep learning

Abstract

Research and development of next-generation automated construction systems centered on automation technology for construction machinery is underway. Among these, automated bulldozers perform the task of spreading materials that have been unloaded by large dump trucks. By understanding the shape and volume of the unloaded materials, further improvements in the accuracy of the spreading operation can be expected. When measuring the unloaded materials from sensors installed on an automatic bulldozer, it was difficult to accurately determine the shape and volume of the materials. This is because there is a large quantity of materials, and they are shaped like a mountain, making it impossible for the automatic bulldozer to measure the rear part of the materials. Therefore, we developed a method to estimate the shape and volume of unloaded materials using deep learning, and the results of precision verification and demonstration experiments in construction site showed an average height error of 0.049m for the shape and a volume error of 5%, confirming its usefulness.