端到端学习多尺度高精度地形可通行性地图，支持高速越野自主导航。
RoadRunner M&M -- Learning Multi-range Multi-resolution Traversability Maps for Autonomous Off-road Navigation
- 输入多视角图像与激光体素图，端到端预测50米和100米范围的地形与可通行性
- 在100米距离上，地形重建提升50%，可通行性估计提升30%，实时运行且覆盖区域多30%
- 首次实现闭环高速越野导航，可在未见复杂地形中泛化
自动驾驶机器人在非结构化环境中的导航需要全面理解地形几何与可通行性。长距离下感知条件退化、几何信息稀疏，尤其在高速行驶时挑战巨大。此外，传感至建图的延迟和前瞻地图范围限制了车辆最大速度。基于先前工作RoadRunner，本文提出RoadRunner (M&M)，一种端到端学习框架，直接输入多视角图像和激光体素图，预测50米与100米范围、0.2米与0.8米分辨率的可通行性与高程地图。方法通过自监督训练，利用现有可通行性估计模块（X-Racer）事后融合预测与卫星数字高程图生成密集监督信号。实验表明，相比RoadRunner，该方法在高程映射上提升达50%，可通行性估计提升30%，且比X-Racer多覆盖30%区域，同时保持实时性能。在多种分布外数据集上的测试显示，该数据驱动方法开始泛化至全新非结构化环境。将本框架集成至路径规划器闭环中，成功演示了在真实复杂环境中高速自主越野导航。
原文摘要 · Abstract (English)
Autonomous robot navigation in off-road environments requires a comprehensive understanding of the terrain geometry and traversability. The degraded perceptual conditions and sparse geometric information at longer ranges make the problem challenging especially when driving at high speeds. Furthermore, the sensing-to-mapping latency and the look-ahead map range can limit the maximum speed of the vehicle. Building on top of the recent work RoadRunner, in this work, we address the challenge of long-range (100 m) traversability estimation. Our RoadRunner (M&M) is an end-to-end learning-based framework that directly predicts the traversability and elevation maps at multiple ranges (50 m, 100 m) and resolutions (0.2 m, 0.8 m) taking as input multiple images and a LiDAR voxel map. Our method is trained in a self-supervised manner by leveraging the dense supervision signal generated by fusing predictions from an existing traversability estimation stack (X-Racer) in hindsight and satellite Digital Elevation Maps. 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. Experiments on various out-of-distribution datasets also demonstrate that our data-driven approach starts to generalize to novel unstructured environments. We integrate our proposed framework in closed-loop with the path planner to demonstrate autonomous high-speed off-road robotic navigation in challenging real-world environments. Project Page: https://leggedrobotics.github.io/roadrunner_mm/
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