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.