Bending Path Understanding Based on Angle Projections in Field Environments

Abstract Scene understanding is a core problem for field robots. However, many unsolved problems, like understanding bending paths, severely hinder the implementation due to varying illumination, irregular features and unstructured boundaries in field environments. Traditional three-dimensional(3D) environmental perception from 3D point clouds or fused sensors are costly and account poorly for field unstructured semantic information. In this paper, we propose a new methodology to understand field bending paths and build their 3D reconstruction from a monocular camera without prior training. Bending angle projections are assigned to clusters. Through compositions of their sub-clusters, bending surfaces are estimated by geometric inferences. Bending path scenes are approximated bending structures in the 3D reconstruction. Understanding sloping gradient is helpful for a navigating mobile robot to automatically adjust their speed. Based on geometric constraints from a monocular camera, the approach requires no prior training, and is robust to varying color and illumination. The percentage of incorrectly classified pixels were compared to the ground truth. Experimental results demonstrated that the method can successfully understand bending path scenes, meeting the requirements of robot navigation in an unstructured environment.