Robust Pose Estimation and Size Classification for Unknown Dump Truck Using Normal Distribution Transform

Labor shortage has been a severe problem in the Japanese construction industry, and the automation of construction work has been in high demand. One of the needs is the automation of soil loading onto dump trucks. This task requires pose estimation and size classification of the dump trucks to determine the appropriate loading position and volume. At actual construction sites, specifications of dump trucks are not always known in advance. However, most of the existing methods cannot robustly estimate the pose and the size of such unknown dump trucks. To address this issue, we propose a two-stage method that estimates the pose of dump trucks and then classifies their size categories. We use Normal Distribution Transform (NDT) for pose estimation of dump trucks. Specifically, we utilize NDT templates of dump trucks which distinguish global differences among size categories and simultaneously absorb local shape variations within each category. The proposed method is evaluated by data in a real-world environment. The proposed method appropriately estimates the pose of dump trucks under various settings of positions and orientations. In addition, the method correctly classifies the observed dump truck with all three predefined size categories. Furthermore, the computation time is approximately 0.13 s, which is sufficiently short for practical operation. These results indicate that the method will contribute to the automation of soil loading onto dump trucks with unknown specifications.