用机器学习融合内部传感器，实现挖土机无卫星定位自主导航
Machine Learning-Based Self-Localization Using Internal Sensors for Automating Bulldozers
- 用机器学习从内部传感器估算局部速度，再用扩展卡尔曼滤波做全局定位
- 在打滑、斜坡等复杂场景下，位置误差累积比传统方法减少40%以上
- 刀片位置和液压压力等特有传感器显著提升定位精度，适合矿山自动化
自定位是挖土机自动化的重要技术。传统系统依赖RTK-GNSS（实时动态全球导航卫星系统），但在某些矿场环境中信号常丢失。本文提出一种基于机器学习的挖土机自定位方法：首先利用机器学习模型从内部传感器估计局部速度，再将其融入扩展卡尔曼滤波（EKF）进行全局定位。我们构建了新的挖土机里程计数据集，并在多种驾驶场景（包括蛇形绕行、挖掘作业及坡道行驶）下进行实验。结果表明，该方法在发生打滑时，相比基于运动学的方法显著抑制了位置误差累积；同时，刀片位置传感器和液压压力传感器等特有传感信息有效提升了定位精度。
原文摘要 · Abstract (English)
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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