Self-localization is an important technology for automating bulldozers. Conventional bulldozer self-localization systems rely on RTK-GNSS (Real Time Kinematic-Global Navigation Satellite Systems). However, RTK-GNSS signals are sometimes lost in certain mining conditions. Therefore, self-localization methods that do not depend on RTK-GNSS are required. In this paper, we propose a machine learning-based self-localization method for bulldozers. The proposed method consists of two steps: estimating local velocities using a machine learning model from internal sensors, and incorporating these estimates into an Extended Kalman Filter (EKF) for global localization. We also created a novel dataset for bulldozer odometry and conducted experiments across various driving scenarios, including slalom, excavation, and driving on slopes. The result demonstrated that the proposed self-localization method suppressed the accumulation of position errors compared to kinematics-based methods, especially when slip occurred. Furthermore, this study showed that bulldozer-specific sensors, such as blade position sensors and hydraulic pressure sensors, contributed to improving self-localization accuracy.
@article{arxiv.2506.07271,
title = {Machine Learning-Based Self-Localization Using Internal Sensors for Automating Bulldozers},
author = {Hikaru Sawafuji and Ryota Ozaki and Takuto Motomura and Toyohisa Matsuda and Masanori Tojima and Kento Uchida and Shinichi Shirakawa},
journal= {arXiv preprint arXiv:2506.07271},
year = {2025}
}