Forest road surface detection using LiDAR-SLAM and U-Net

Abstract

Autonomous road surface following on forest roads by mobile robots and forestry vehicles carrying large logs is required for work in the forestry industry. In such situations, the detection of a passable road surface has to respond to changes in road geometry, surface conditions, and lighting. In this paper, we propose an efficient road detection method using LiDAR-SLAM and U-Net architectures: LiDAR-SLAM can accurately estimate the shape of the road in response to environmental changes, while U-Net architectures can efficiently estimate the edge of the road in a forest road. In the experiment, we used IoU (Intersection over Union) to evaluate the accuracy of the road surface detection in passing through a forest road. As a result, the proposed method achieved an IoU value of more than 90.2%.