Understanding terrain topology at long-range is crucial for the success of off-road robotic missions, especially when navigating at high-speeds. LiDAR sensors, which are currently heavily relied upon for geometric mapping, provide sparse measurements when mapping at greater distances. To address this challenge, we present a novel learning-based approach capable of predicting terrain elevation maps at long-range using only onboard egocentric images in real-time. Our proposed method is comprised of three main elements. First, a transformer-based encoder is introduced that learns cross-view associations between the egocentric views and prior bird-eye-view elevation map predictions. Second, an orientation-aware positional encoding is proposed to incorporate the 3D vehicle pose information over complex unstructured terrain with multi-view visual image features. Lastly, a history-augmented learn-able map embedding is proposed to achieve better temporal consistency between elevation map predictions to facilitate the downstream navigational tasks. 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. Furthermore, the method is qualitatively and quantitatively compared against the current state-of-the-art methods. Extensive field experiments demonstrate that our method surpasses baseline models in accurately predicting terrain elevation while effectively capturing the overall terrain topology at long-ranges. Finally, ablation studies are conducted to highlight and understand the effect of key components of the proposed approach and validate their suitability to improve offroad robotic navigation capabilities.

翻译：理解远距离地形拓扑结构对于越野机器人的成功任务至关重要，尤其是在高速导航场景中。当前严重依赖激光雷达传感器进行几何建图时，远距离建图仅能获取稀疏测量数据。为解决这一挑战，我们提出一种基于学习的新方法，能够仅利用车载自视角图像实时预测远距离地形高程图。该方法包含三个核心模块：首先，引入基于Transformer的编码器，学习自视角视图与先验鸟瞰高程图预测之间的跨视角关联；其次，提出方向感知位置编码，将复杂非结构化地形中的三维车辆位姿信息与多视角视觉图像特征相结合；最后，设计历史增强可学习建图嵌入，实现高程图预测的时序一致性，从而支持下游导航任务。我们利用真实越野驾驶数据，通过实验验证了该方法在复杂非结构化地形中自主越野机器人导航的适用性。此外，该方法与当前最先进方法进行了定性与定量比较。大量实地实验表明，本方法在准确预测地形高程的同时，能有效捕捉远距离地形整体拓扑结构，性能超越基线模型。最后，通过消融实验凸显并解析了关键组件的贡献，验证了其提升越野机器人导航能力的适用性。