Machine Learning-Based Self-Localization Using Internal Sensors for Automating Bulldozers
This work addresses a domain-specific problem for automating bulldozers in mining, offering an incremental improvement by combining machine learning with existing filtering techniques.
The paper tackled the problem of bulldozer self-localization in mining conditions where RTK-GNSS signals are unreliable by proposing a machine learning-based method that uses internal sensors to estimate velocities and integrates them with an Extended Kalman Filter, resulting in suppressed position error accumulation compared to kinematics-based methods, especially during slip events.
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.