CAVEAT: 地図なし空中探査のためのリカレント多モーダル拡散プランニング
CAVEAT: Recurrent Multimodal Diffusion Planning for Mapless Aerial Exploration
この論文は、UAVの探査経路生成を、グローバルマップを保持せずに、LiDAR・視覚・姿勢の多モーダル観測と固定次元のリカレント内部状態から行う拡散ポリシーCAVEATを提案している。シミュレーションと実機実験で有効性を示した。
著者: Steven Visch, Nicolò Botteghi, Antonio Franchi, Barbara Bazzana
分類: cs.RO, cs.LG
原文アブストラクト
Can exploratory UAV waypoint sequences be generated from multimodal onboard observations and a fixed-dimensional recurrent internal state without maintaining a persistent global map in the deployed policy? We investigate this question through CAVEAT, a diffusion policy conditioned on a recurrent internal state updated from fused LiDAR, visual, and pose features and trained from trajectories generated by the map-based FUELv2 expert. Rolling inference partially warm-starts consecutive predictions, while a temporary local signed distance field provides heuristic obstacle guidance. Simulation results evaluate both inference mechanisms and compare CAVEAT with its demonstration-generating expert. Proof-of-concept experiments on a Flyability Elios 3 demonstrate partial exploration of a previously unseen indoor environment and target-directed visual servoing using a separately trained policy.