Autonomous off-road navigation requires robots to estimate terrain traversability from onboard sensors and plan accordingly. Conventional approaches typically rely on sampling-based planners such as MPPI to generate short-term control actions that aim to minimize traversal time and risk measures derived from the traversability estimates. These planners can react quickly but optimize only over a short look-ahead window, limiting their ability to reason about the full path geometry, which is important for navigating in challenging off-road environments. Moreover, they lack the ability to adjust speed based on the terrain bumpiness, which is important for smooth navigation on challenging terrains. In this paper, we introduce TRAIL (Traversability with an Implicit Learned Representation), an off-road navigation framework that leverages an implicit neural representation to continuously parameterize terrain properties. This representation yields spatial gradients that enable integration with a novel gradient-based trajectory optimization method that adapts the path geometry and speed profile based on terrain traversability.

翻译：自主离线导航要求机器人通过机载传感器估计地形可通行性并据此进行规划。传统方法通常依赖基于采样的规划器（如MPPI）生成短期控制动作，旨在最小化由可通行性估计导出的穿越时间和风险度量。这些规划器能够快速响应，但仅在短前瞻窗口内进行优化，限制了其对完整路径几何的推理能力，而这在具有挑战性的离线环境中至关重要。此外，它们缺乏根据地形的颠簸程度调整速度的能力，这对于在复杂地形上实现平稳导航非常重要。本文提出TRAIL（基于隐式学习表征的可通行性），一种离线导航框架，利用隐式神经表征连续参数化地形属性。该表征生成的空间梯度可与一种新颖的基于梯度的轨迹优化方法相结合，从而根据地形的可通行性自适应调整路径几何与速度分布。