A Roadheader Positioning Method Based on Multi-Sensor Fusion

Abstract

In coal mines, accurate positioning is vital for roadheader equipment. However, most roadheaders use a standalone strapdown inertial navigation system (SINS) which faces challenges like error accumulation, drift, initial alignment needs, temperature sensitivity, and the demand for high-quality sensors. In this paper, a roadheader Visual–Inertial Odometry (VIO) system is proposed, combining SINS and stereo visual odometry to adjust to coal mine environments. Given the inherently dimly lit conditions of coal mines, our system includes an image-enhancement module to preprocess images, aiding in feature matching for stereo visual odometry. Additionally, a Kalman filter merges the positional data from SINS and stereo visual odometry. When tested against three other methods on the KITTI and EuRoC datasets, our approach showed notable precision on the EBZ160M-2 Roadheader, with attitude errors less than 0.2751° and position discrepancies within 0.0328 m, proving its advantages over SINS.