地球外航行と経路計画のためのニューラルラディアンスマップ
Neural Radiance Maps for Extraterrestrial Navigation and Path Planning
火星探査車などの自律走行車のために、NeRFを用いて構築した3Dマップを活用し、局所的なコスト観測を大域的に補間して経路計画を行うフレームワークを提案した。シミュレーションで低コストかつ高成功率の経路計画を実証した。
詳しい要約
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著者: Adam Dai, Shubh Gupta, Grace Gao
分類: cs.RO, cs.CV
原文アブストラクト
Autonomous vehicles such as the Mars rovers currently lead the vanguard of surface exploration on extraterrestrial planets and moons. In order to accelerate the pace of exploration and science objectives, it is critical to plan safe and efficient paths for these vehicles. However, current rover autonomy is limited by a lack of global maps which can be easily constructed and stored for onboard re-planning. Recently, Neural Radiance Fields (NeRFs) have been introduced as a detailed 3D scene representation which can be trained from sparse 2D images and efficiently stored. We propose to use NeRFs to construct maps for online use in autonomous navigation, and present a planning framework which leverages the NeRF map to integrate local and global information. Our approach interpolates local cost observations across global regions using kernel ridge regression over terrain features extracted from the NeRF map, allowing the rover to re-route itself around untraversable areas discovered during online operation. We validate our approach in high-fidelity simulation and demonstrate lower cost and higher percentage success rate path planning compared to various baselines.
