CAVEAT：用于无地图空中探索的循环多模态扩散规划
CAVEAT: Recurrent Multimodal Diffusion Planning for Mapless Aerial Exploration
- University of Twente（特文特大学）
- Politecnico di Milano（米兰理工大学）
- Sapienza University of Rome（罗马大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
CAVEAT提出一种基于循环内部状态和多模态观测的扩散策略，无需全局地图即可生成探索航点，并在仿真和真实无人机上验证了其有效性。
AI中文摘要：
探索性无人机航点序列能否由多模态机载观测和固定维度的循环内部状态生成，而无需在部署策略中维护持久全局地图？我们通过CAVEAT研究这一问题，这是一种扩散策略，其条件基于从融合的激光雷达、视觉和位姿特征更新的循环内部状态，并由基于地图的FUELv2专家生成的轨迹训练。滚动推理部分热启动连续预测，而临时局部符号距离场提供启发式障碍物引导。仿真结果评估了两种推理机制，并将CAVEAT与其生成演示的专家进行比较。在Flyability Elios 3上的概念验证实验展示了在未见过的室内环境中的部分探索，以及使用单独训练策略的目标导向视觉伺服。
英文摘要：
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.
