Machine Learning-Based Self-Localization Using Internal Sensors for Automating Bulldozers
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866909642667327488 |
|---|---|
| author | Sawafuji, Hikaru Ozaki, Ryota Motomura, Takuto Matsuda, Toyohisa Tojima, Masanori Uchida, Kento Shirakawa, Shinichi |
| author_facet | Sawafuji, Hikaru Ozaki, Ryota Motomura, Takuto Matsuda, Toyohisa Tojima, Masanori Uchida, Kento Shirakawa, Shinichi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_07271 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning-Based Self-Localization Using Internal Sensors for Automating Bulldozers Sawafuji, Hikaru Ozaki, Ryota Motomura, Takuto Matsuda, Toyohisa Tojima, Masanori Uchida, Kento Shirakawa, Shinichi Robotics Machine Learning 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. |
| title | Machine Learning-Based Self-Localization Using Internal Sensors for Automating Bulldozers |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2506.07271 |