无需全局地图，用多模态感知实现无人机自主探索
CAVEAT: Recurrent Multimodal Diffusion Planning for Mapless Aerial Exploration
- 基于循环内部状态和融合感知生成航点序列
- 仿真中实现部分探索，实机验证目标视觉伺服
- 适合无地图环境下的无人机自主导航任务
能否仅依赖机载多模态观测与固定维度的循环内部状态，在不维护持久全局地图的情况下生成无人机探索航点？我们通过CAVEAT解决该问题：该扩散策略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。滚动推理部分预热连续预测，临时局部符号距离场提供障碍物引导。仿真结果评估了两种推理机制，并将CAVEAT与生成演示的专家进行对比。概念验证实验在Flyability Elios 3上完成，实现了对未见过室内环境的部分探索及使用独立训练策略的目标导向视觉伺服。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。