Accurate and robust navigation in unstructured environments requires fusing data from multiple sensors. Such fusion ensures that the robot is better aware of its surroundings, including areas of the environment that are not immediately visible, but were visible at a different time. To solve this problem, we propose a method for traversability prediction in challenging outdoor environments using a sequence of RGB and depth images fused with pose estimations. Our method, termed WayFASTER (Waypoints-Free Autonomous System for Traversability with Enhanced Robustness), uses experience data recorded from a receding horizon estimator to train a self-supervised neural network for traversability prediction, eliminating the need for heuristics. Our experiments demonstrate that our method excels at avoiding geometric obstacles, and correctly detects that traversable terrains, such as tall grass, can be navigable. By using a sequence of images, WayFASTER significantly enhances the robot's awareness of its surroundings, enabling it to predict the traversability of terrains that are not immediately visible. This enhanced awareness contributes to better navigation performance in environments where such predictive capabilities are essential.

翻译：在非结构化环境中实现精确鲁棒的导航需要融合多传感器数据。这种融合确保机器人能够更好地感知周围环境，包括那些当前不可见但曾在不同时间点可见的环境区域。为解决该问题，我们提出了一种利用RGB与深度图像序列融合位姿估计的可通行性预测方法，适用于具有挑战性的户外环境。本方法命名为WayFASTER（具备增强鲁棒性的无航点自主可通行性系统），采用退避地平线估计器记录的经验数据训练自监督神经网络进行可通行性预测，从而消除了对启发式规则的需求。实验表明，本方法在规避几何障碍物方面表现优异，并能正确判定高草丛等可通行地形的通过性。通过利用图像序列，WayFASTER显著增强了机器人对周围环境的感知能力，使其能够预测当前不可见地形的可通行性。这种增强的感知能力有助于在需要此类预测功能的场景中实现更优的导航性能。