3D Clearance Control: Automatic Roadside Vegetation Maintenance

Overgrown roadside vegetation poses a danger to road users by obstructing the visibility of the road and potentially obscuring signs or other traffic participants. Thus, regulations clearly define the height above the road that must not be obscured. As manually identifying such incidents is time-consuming, we propose 3D Clearance Control: a pipeline that automatically detects vegetation in need of trimming. Our system is based on LiDAR point clouds, which give access to accurate position and height information. It comprises four main steps: the semantic segmentation of the point cloud, the aggregation of scans within a scene, the estimation of road boundaries, and the creation of the volume representing the clearance gauge. We developed a modular process to perform a comprehensive evaluation combining different segmentation models and road boundary approximation methods. We mea-sure the accuracy and computing times on three widely used street-level datasets: SemanticKITTI, NuScenes, and PandaSet. We achieved an mIoU of 67.6 on our annotated test scenes and a speed increase of 52.7% compared to previous systems.