Off-Road Navigation via Implicit Neural Representation of Terrain Traversability
🤖 AI Summary
Traditional off-road navigation methods suffer from myopic planning and decoupled speed-path optimization, hindering simultaneous achievement of globally geometrically feasible paths and terrain-adaptive velocity control. To address this, we propose TRAIL: a framework that employs Implicit Neural Representations (INRs) to continuously model terrain traversability and explicitly provide spatial gradients; it then formulates a differentiable trajectory optimization problem that jointly optimizes path geometry and velocity distribution. TRAIL integrates multi-sensor inputs into an end-to-end differentiable global path inference pipeline. Experiments demonstrate that TRAIL significantly improves navigation smoothness, safety, and long-range traversal efficiency over rugged terrain. It enables dynamic, terrain-aware velocity adaptation and generates complex geometrically compliant paths—without requiring explicit map pre-processing or hand-crafted cost functions.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Optimizing short-term control actions for autonomous off-road navigation
Reasoning about full path geometry in challenging environments
Adjusting speed based on terrain bumpiness for smooth navigation
Innovation
Methods, ideas, or system contributions that make the work stand out.
Implicit neural representation of terrain traversability
Gradient-based trajectory optimization method
Adapts path geometry and speed profile
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