Autonomous off-road driving requires understanding traversability, which refers to the suitability of a given terrain to drive over. When offroad vehicles travel at high speed ($>10m/s$), they need to reason at long-range ($50m$-$100m$) for safe and deliberate navigation. Moreover, vehicles often operate in new environments and under different weather conditions. LiDAR provides accurate estimates robust to visual appearances, however, it is often too noisy beyond 30m for fine-grained estimates due to sparse measurements. Conversely, visual-based models give dense predictions at further distances but perform poorly at all ranges when out of training distribution. To address these challenges, we present ALTER, an offroad perception module that adapts-on-the-drive to combine the best of both sensors. Our visual model continuously learns from new near-range LiDAR measurements. This self-supervised approach enables accurate long-range traversability prediction in novel environments without hand-labeling. Results on two distinct real-world offroad environments show up to 52.5% improvement in traversability estimation over LiDAR-only estimates and 38.1% improvement over non-adaptive visual baseline.

翻译：自主越野驾驶需要理解可通过性，即特定地形是否适合行驶。当越野车辆高速行驶（>10m/s）时，需进行长距离（50m-100m）推理以实现安全而审慎的导航。此外，车辆常在新环境及不同天气条件下运行。激光雷达能提供对视觉外观具有鲁棒性的精确估计，但受稀疏测量影响，超过30米时噪声过大而难以进行细粒度估计。相比之下，基于视觉的模型可在更远距离给出密集预测，但当处于训练分布外时，所有距离范围的性能均会下降。为应对这些挑战，我们提出ALTER——一种在行驶中自适应的越野感知模块，可融合两类传感器的优势。我们的视觉模型持续从近距离激光雷达测量中学习。这种自监督方法能在无人工标注条件下，实现新颖环境中精确的长距离可通过性预测。在两个真实越野环境中的实验结果表明，相较于纯激光雷达估计，可通过性估计性能提升高达52.5%；相较于非自适应视觉基准方法，性能提升达38.1%。