Adaptive Localization Algorithm for Autonomous Mining Trucks with Motion Distortion Removal Tianjin University

Format:

Book

Conference/Event

Author/Creator:

Meng, Chunyang, author.

Contributor:

Kang, Song

Xie, Hui

Xing, Wanyong

Conference Name:

SAE 2024 Vehicle Powertrain Diversification Technology Forum (2024-12-06 : Xi'An, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

During the operation of autonomous mining trucks in the process of crushing stones, the GPS signal is lost due to signal blockage by the crushing workshop. Simultaneous Localization and Mapping (SLAM) becomes critical for ensuring accurate vehicle positioning and smooth operation. However, the bumpy road conditions and the scarcity of plane and corner feature points in mining environments pose challenges to SLAM algorithms in practical applications, such as pose jumps and insufficient positioning accuracy. To address this, this paper proposes a high-precision positioning algorithm based on inertial navigation 3D signals, incorporating point cloud motion distortion correction, a vehicle roll model, and an Adaptive Kalman Filter (AKF). The goal is to improve the positioning accuracy and stability of autonomous mining trucks in complex scenarios. This paper utilizes real-world operational data from mining vehicles and adopts a 3D point cloud motion distortion correction algorithm to mitigate the impact of bumpy roads on positioning accuracy. Additionally, a dynamic model that considers vehicle sideslip is integrated, and the feedback from the Inertial Measurement Unit (IMU) is fused with the positioning results obtained from LiDAR point cloud registration using Normal Distributions Transform (NDT) through an Adaptive Extended Kalman Filter (AEKF). Furthermore, an error analysis model is designed to enable adaptive adjustment of the algorithm, and the performance of the NDT algorithm is enhanced in open, feature-scarce environments through LiDAR point cloud fusion techniques. Simulation results show that the positioning stability on bumpy roads is improved by approximately 21.2%. The improved algorithm effectively suppresses pose jumps during large turn radii, reducing the average error by 5.94% compared to the traditional Kalman Filter (KF). Moreover, the algorithm demonstrates higher positioning accuracy and stability under sensor failures and adverse weather conditions

Notes:

Vendor supplied data

Publisher Number:

2025-01-7027

Access Restriction:

Restricted for use by site license