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<p>To address the critical technical challenge of positioning failure in unmanned mining trucks induced by global navigation satellite system (GNSS) signal attenuation in complex open-pit coal mine environments, particularly the significant trajectory oscillation during cornering in IMU-LiDAR integrated navigation under the simulated GNSS outage condition (with a focus on the challenging scenario of GNSS outage), this study proposes a collaborative positioning method integrating inertial measurement unit (IMU) and LiDAR based on the extended Kalman filter (EKF). A nonlinear system fusion positioning model was established, where IMU high-frequency motion estimation serves as the prediction equation and LiDAR point cloud matching (adopting the normal distribution transformation, NDT, algorithm) acts as the observation equation. The EKF algorithm performs optimal estimation on both the position estimation results of IMU and the pose information of LiDAR obtained via NDT, effectively suppressing the cumulative errors of IMU and the matching jitter of LiDAR under feature degradation scenarios. To verify the algorithm’s performance comprehensively, a standardized experimental vehicle platform was constructed, and tests were conducted under multi-route, multi-speed, and repeated trial conditions. To simulate a representative and controllable weak-GNSS condition, the GNSS signal was programmatically blocked to emulate a complete outage scenario. Results indicate that while individual positioning algorithms exhibit significant tracking jitter in curved sections, the proposed IMU-LiDAR fusion trajectory closely matches the preset path. Compared to standalone IMU/LiDAR algorithms, the fusion method reduces average offset by up to 24.51% and standard deviation by up to 34.15%; when compared with mainstream open-source integrated navigation algorithms such as FAST-LIO2 and LIO-SAM, it also demonstrates superior positioning accuracy and trajectory stability. These research findings provide reliable technical support for intelligent construction in open-pit coal mines.</p>

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Research on IMU-LiDAR collaborative positioning method for unmanned mine truck under weak GNSS conditions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月11日

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  • Guilin Hu,
  • Jun Wang,
  • Qiqi Li,
  • Hongyu Zhang,
  • Shaohua Sun,
  • Zhiyong Yang,
  • Zhiqun Wang

摘要

To address the critical technical challenge of positioning failure in unmanned mining trucks induced by global navigation satellite system (GNSS) signal attenuation in complex open-pit coal mine environments, particularly the significant trajectory oscillation during cornering in IMU-LiDAR integrated navigation under the simulated GNSS outage condition (with a focus on the challenging scenario of GNSS outage), this study proposes a collaborative positioning method integrating inertial measurement unit (IMU) and LiDAR based on the extended Kalman filter (EKF). A nonlinear system fusion positioning model was established, where IMU high-frequency motion estimation serves as the prediction equation and LiDAR point cloud matching (adopting the normal distribution transformation, NDT, algorithm) acts as the observation equation. The EKF algorithm performs optimal estimation on both the position estimation results of IMU and the pose information of LiDAR obtained via NDT, effectively suppressing the cumulative errors of IMU and the matching jitter of LiDAR under feature degradation scenarios. To verify the algorithm’s performance comprehensively, a standardized experimental vehicle platform was constructed, and tests were conducted under multi-route, multi-speed, and repeated trial conditions. To simulate a representative and controllable weak-GNSS condition, the GNSS signal was programmatically blocked to emulate a complete outage scenario. Results indicate that while individual positioning algorithms exhibit significant tracking jitter in curved sections, the proposed IMU-LiDAR fusion trajectory closely matches the preset path. Compared to standalone IMU/LiDAR algorithms, the fusion method reduces average offset by up to 24.51% and standard deviation by up to 34.15%; when compared with mainstream open-source integrated navigation algorithms such as FAST-LIO2 and LIO-SAM, it also demonstrates superior positioning accuracy and trajectory stability. These research findings provide reliable technical support for intelligent construction in open-pit coal mines.