暗黙的ニューラル表現による地形走破性モデリングに基づくオフロードナビゲーション
Off-Road Navigation via Implicit Neural Representation of Terrain Traversability
地形の走破性を暗黙的ニューラル表現で連続場としてモデル化し、その勾配を利用した軌道最適化により経路形状と速度プロファイルを地形に応じて適応させるオフロードナビゲーション手法TRAILを提案した。
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著者: Yixuan Jia, Qingyuan Li, Jonathan P. How
分類: cs.RO
原文アブストラクト
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.
