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

TL;DR

This paper presents a machine learning-based self-localization approach for bulldozers that does not rely on satellite signals, using internal sensors and a novel dataset to improve accuracy in challenging conditions.

Contribution

The study introduces a new self-localization method combining machine learning and EKF, along with a novel dataset for bulldozer odometry, addressing GNSS signal loss issues.

Findings

Reduced position error accumulation compared to kinematic methods

Sensor data like blade position and hydraulic pressure improve accuracy

Effective in various driving scenarios including slopes and excavation

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.