用神经隐式表征建模地形可通行性，实现更平滑的越野路径规划。
Off-Road Navigation via Implicit Neural Representation of Terrain Traversability
- 用隐式神经网络连续表示地形可通行性，支持任意位置查询。
- 结合梯度优化方法，同时调整路径形状与速度分布以适应地形。
- 适合需要长距离规划与振动控制的复杂越野场景导航。
自主越野导航需基于车载传感器估计地形可通行性并据此规划运动。传统方法多依赖采样类规划器（如MPPI），生成短期控制指令以最小化行进时间与基于可通行性估计的风险。此类方法响应迅速，但仅在短视窗内优化，难以考虑完整路径几何，不利于复杂越野环境中的全局决策；且无法根据地形引发的振动动态调节速度，影响平稳性。本文提出TRAIL（Traversability with an Implicit Learned Representation）框架，利用隐式神经表示将地形属性建模为可任意位置查询的连续场。该表示提供空间梯度，支持一种新型基于梯度的轨迹优化方法，可联合调整路径几何与速度曲线以适配地形可通行性。
原文摘要 · Abstract (English)
Autonomous off-road navigation requires robots to estimate terrain traversability from onboard sensors and plan motion 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-induced vibrations, 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 model terrain properties as a continuous field that can be queried at arbitrary locations. 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.
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