Improved 2D Image Segmentation for Rough Terrain Navigation using Synthetic Data

Semantic segmentation of 2D images is a critical capability for Unmanned Ground Vehicle (UGV) navigation. A significant amount of work has been performed in data collection for road rated civilian UGVs, but Army applications are more challenging, requiring algorithms to identify a wider range of terrain and conditions. Acquiring sufficient off-road data is challenging, time intensive, and expensive due to the vast amount of variation in factors such as offroad terrain, lighting conditions, and weather that are not present in on-road applications. Simulators can rapidly synthesize imagery appropriate to target environments that can be used to re-train models for environments with sparse datasets. Here we show that synthetic off-road data generated in simulation improved the performance of a scene segmentation algorithm deployed on a UGV. We discuss solutions to optimize the generation of synthetic data, as well as mixing with real data for autonomous navigation in rough terrain.