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Keywords = autonomous heavy-duty truck localization

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23 pages, 10712 KiB  
Article
A Multi-Sensor Fusion Autonomous Driving Localization System for Mining Environments
by Yi Wang, Chungming Own, Haitao Zhang and Minzhou Luo
Electronics 2024, 13(23), 4717; https://doi.org/10.3390/electronics13234717 - 28 Nov 2024
Viewed by 2279
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Unmanned Vehicles Systems Application)
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