Problem
Research questions and friction points this paper is trying to address.
Recurrent Internal State
Multimodal Observations
Mapless Aerial Exploration
Waypoint Sequences
CAVEAT: Recurrent Multimodal Diffusion Planning for Mapless Aerial Exploration
🤖 AI Summary
研究通过CAVEAT方法，利用多模态观测和循环内部状态生成探索性无人机路径序列，无需维护全局地图。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Recurrent Internal State
Multimodal Fusion
Mapless Exploration
Diffusion Policy
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