Version 1
: Received: 30 October 2024 / Approved: 31 October 2024 / Online: 31 October 2024 (08:46:20 CET)
How to cite:
WANG, Y.; Own, C. M.; Zhang, H. T.; Luo, M. Z. Multi-Sensor Fusion Autonomous Driving Localization System in Mining Environments. Preprints2024, 2024102495. https://doi.org/10.20944/preprints202410.2495.v1
WANG, Y.; Own, C. M.; Zhang, H. T.; Luo, M. Z. Multi-Sensor Fusion Autonomous Driving Localization System in Mining Environments. Preprints 2024, 2024102495. https://doi.org/10.20944/preprints202410.2495.v1
WANG, Y.; Own, C. M.; Zhang, H. T.; Luo, M. Z. Multi-Sensor Fusion Autonomous Driving Localization System in Mining Environments. Preprints2024, 2024102495. https://doi.org/10.20944/preprints202410.2495.v1
APA Style
WANG, Y., Own, C. M., Zhang, H. T., & Luo, M. Z. (2024). Multi-Sensor Fusion Autonomous Driving Localization System in Mining Environments. Preprints. https://doi.org/10.20944/preprints202410.2495.v1
Chicago/Turabian Style
WANG, Y., Hai tao Zhang and Ming zhou Luo. 2024 "Multi-Sensor Fusion Autonomous Driving Localization System in Mining Environments" Preprints. https://doi.org/10.20944/preprints202410.2495.v1
Abstract
We propose a multi-sensor fusion localization framework for autonomous heavy-duty trucks suitable for mining scenarios, which enables high-precision, real-time trajectory generation and map construction. The motion estimated through pre-integration of the Inertial Measurement Unit (IMU) can eliminate distortions in the point cloud and provide an initial guess for LiDAR odometry optimization. The point cloud information obtained from LiDAR can assist in recovering depth information from image features extracted by the monocular camera. To ensure real-time performance, we introduce an iKD-Tree to organize the point cloud data. To address issues arising from bumpy road segments and long-distance driving in practical mining scenarios, we can incorporate a large number of relative and absolute measurements from different sources, such as GPS information and AprilTag-assisted localization data, including loop closure as factors in the system. The proposed method has been extensively evaluated on public datasets and self-collected datasets from mining sites.Our implementation is available at https://github.com/wangyicxy/iLM-slam
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
