Off-Road Terrain Surface Type Recognition Based on Multi-Sensor Information Fusion

作者：Yusen Wu, Dawei Pi, Jiahui Feng, Guangda Li · 年份：2024 · DOI：10.1109/aiac63745.2024.10899456 · 被引用次数：1 · 研究领域：Remote Sensing and Land Use、Technology and Security Systems、Advanced Algorithms and Applications

This study aims to detect the surface type of the road ahead and the distance between the vehicle and the changing road surface in an off-road environment. The goal is achieved by fusing image and point cloud data. The specific process is as follows: acquire camera images and radar point cloud data, segment the images using the DeepLabv3+ network, and perform multi-frame point cloud fusion. The fused data is then transformed through coordinate conversion to assign color information to the point cloud. The 3D point cloud is flattened, and a custom algorithm is used to detect the road surface type. The experiment shows that the DeepLabv3+ model performs excellently across various evaluation metrics. In the multi-frame point cloud fusion, increasing the number of frames results in higher point cloud density, but issues such as point cloud overlap and longer algorithm execution time arise. A fusion of 2 frames is selected as the optimal approach. Ultimately, the experimental system was successfully built, effectively detecting the road surface type.