Surface Roughness Estimation for Terrain Perception

Ground terrain perception has become the primary visual task for the robust navigation of intelligent systems in unstructured outdoor environments. However, complex ter-rain poses a significant challenge to vision-based perception. This work introduces a novel estimation task using RGB images to facilitate low-cost terrain perception in extracting surface roughness information. The proposed task presents both semantic-aware and edge-aware roughness descriptors at the pixel level instead of a single value for a given image. To promote the research on the proposed novel terrain roughness estimation task, we introduce a multimodal synthetic dataset for terrain perception in outdoor scenes, containing multiple terrain categories, diverse viewpoints, different lighting and weather conditions, as well as semantic and roughness annotations. Additionally, inspired by computer graphics, we introduce TRENet, a roughness estimation architecture to model the intrinsic correlation of depth-normal-roughness. We also perform ablation studies on the effect of each component and diverse types of inputs. Extensive evaluations and comparisons demonstrate that our method can effectively predict pixel-wise terrain surface roughness with high accuracy.