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arXiv · 2506.07271

Machine Learning-Based Self-Localization Using Internal Sensors for Automating Bulldozers

Hikaru Sawafuji·Ryota Ozaki·Takuto Motomura·Toyohisa Matsuda·Masanori Tojima·Kento Uchida·Shinichi Shirakawa
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Abstract

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.

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Hikaru Sawafuji, Ryota Ozaki, Takuto Motomura, Toyohisa Matsuda, Masanori Tojima, Kento Uchida, Shinichi Shirakawa. 2025-06-08. Machine Learning-Based Self-Localization Using Internal Sensors for Automating Bulldozers. https://arxiv.org/abs/2506.07271

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