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Found 5 papers, 0 papers with code
Few-shot Semantic Learning for Robust Multi-Biome 3D Semantic Mapping in Off-Road Environments
Off-road environments pose significant perception challenges for high-speed autonomous navigation due to unstructured terrain, degraded sensing conditions, and domain-shifts among biomes.
RoadRunner M&M -- Learning Multi-range Multi-resolution Traversability Maps for Autonomous Off-road Navigation
RoadRunner M&M achieves a significant improvement of up to 50% for elevation mapping and 30% for traversability estimation over RoadRunner, and is able to predict in 30% more regions compared to X-Racer while achieving real-time performance.
RoadRunner -- Learning Traversability Estimation for Autonomous Off-road Driving
Furthermore, RoadRunner improves the system latency by a factor of roughly 4, from 500 ms to 140 ms, while improving the accuracy for traversability costs and elevation map predictions.
Pixel to Elevation: Learning to Predict Elevation Maps at Long Range using Images for Autonomous Offroad Navigation
We experimentally validate the applicability of our proposed approach for autonomous offroad robotic navigation in complex and unstructured terrain using real-world offroad driving data.
TRADE: Object Tracking with 3D Trajectory and Ground Depth Estimates for UAVs
We propose TRADE for robust tracking and 3D localization of a moving target in cluttered environments, from UAVs equipped with a single camera.
