Body-aware LiDAR framework for real-time excavator bucket pose estimation

Accurate excavator bucket pose estimation is fundamental for automated earthmoving and precision grading, yet reliable real-time feedback remains difficult to obtain on dynamic construction sites. Proprioceptive sensors suffer from cumulative drift caused by mechanical backlash and structural deformation, while external vision or radar systems require fixed infrastructure and are prone to occlusion. This paper presents a body-aware LiDAR framework that uses an onboard LiDAR sensor as an internal observer of the excavator to estimate bucket pose in real time. The method follows a predict-observe-update paradigm that integrates kinematic motion priors, semantic point-cloud estimation based on an enhanced PointNet++, and model-based ICP registration with adaptive gating for robust tracking under dust, soil occlusion, and sparse observations. Field experiments on a SANY SY19E excavator demonstrate 1.2 cm positional accuracy and 2.1° orientation accuracy at 9.7 Hz, while capping maximum errors under favorable scenarios at 1.9 cm and 7.7°, providing a practical onboard estimation solution for future closed-loop control of construction machinery.