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Semantic segmentation of 2D images is a critical capability for Unmanned Ground Vehicles (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 off- road 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.
Uplinger et al. (2024) studied this question.