关键词: 机场停机坪, 激光雷达, 惯性测量单元, 回环检测, 实验平台, 传感器融合

Abstract:

The airport apron environment features a large scene range and sparse feature points. Increased random feature points can the lead to mapping distortion and odometer drift. This paper presents improvements based on the tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping （LIO-SAM） method. Firstly， the external and internal parameter calibration of Light Detection and Ranging （LiDAR） and Inertial Measurement Unit （IMU） is conducted， the LiDAR point cloud format is converted into the Velodyne format， and adaptive feature point curvature calculation is used to obtain accurate curvature at various distances. Then， Scan-Context loop detection is introduced to enhance loop efficiency and mapping accuracy， so as to generate a high-precision apron environment point cloud map. Two datasets， park roads and a simulated apron， are collected using the experimental platform for comparison. The proposed algorithm is evaluated against the A-LOAM， LeGO-LOAM， and LIO-SAM algorithms， using GNSS data as trajectory ground truth. Results show that the improved multi-sensor fusion algorithm reduces the mean absolute pose error by 13.8% compared to the original LIO-SAM algorithm in apron environments. The proposed algorithm also has the lowest mean absolute pose error among mainstream algorithms， providing higher mapping accuracy， reducing cumulative error， and better restoring the real scene of the apron.

Key words: airport apron, LiDAR, IMU, loop detection, experimental platform, sensor fusion