发表机构
Qiyuan Lab; Tsinghua University（启元实验室; 清华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出贝叶斯空间世界模型ALONE，用于间歇感知下的无人机导航，仅在必要时请求观测，在模拟与真实场景中均实现高导航成功率且大幅降低观测需求。
AI 中文摘要
机器人导航通常采用广覆盖、高频次的感知来降低部分可观测性；但当另一项任务临时将共享传感器从导航中转移时，这种依赖会变得受限，导致与导航相关的观测中断。我们研究间歇感知下的导航：基于内部空间信念行动，仅在执行需要新观测时才再次感知，从而可在导航观测间隙将共享传感器释放给其他任务。ALONE是一种贝叶斯空间世界模型，它利用执行的动作传播结构化空间信念，并通过选择性获取的观测对其进行修正；针对常见几何结构的学习先验可从可用观测历史中推断未观测到的结构。它将信念解码为运动规划模块所需的空间估计，并预测表达该估计准确性置信度的可靠性图。ALONE仅在可靠性不足阻碍导航且新证据应提升相关区域空间信息可靠性时才请求观测；否则，它继续基于传播的信念行动。我们将ALONE实例化为无人机导航，采用间歇单目相机深度图像。在两个模拟场景族中，它在10 Hz决策频率下分别达到98%和97%的闭环成功率；在成功试验中，需要新深度观测的决策步骤中位数占比仅分别为0.9%和1.3%，证明了高导航成功率且观测需求大幅降低。真实室内飞行实验进一步验证了间歇深度观测下的导航，10次试验全部成功。
英文摘要
Robot navigation commonly uses wide-coverage, high-frequency sensing to reduce partial observability; this reliance becomes restrictive when another task temporarily redirects a shared sensor from navigation, interrupting navigation-relevant observations. We study navigation under intermittent perception: acting from an internal spatial belief and looking again only when execution needs a new observation, potentially freeing the shared sensor for other tasks between navigation observations. ALONE, a Bayesian spatial world model, propagates a structured spatial belief using executed actions and corrects it with selectively acquired observations; learned priors over common geometric structures infer unobserved structure from available observation history. It decodes the belief into a spatial estimate for the motion-planning module and predicts a reliability map expressing confidence in the estimate's accuracy. ALONE requests an observation only if insufficient reliability hinders navigation and new evidence should make relevant-region spatial information more reliable; otherwise, it continues acting from the propagated belief. We instantiate ALONE for drone navigation with intermittent single-camera depth images. Across two simulated scene families, it achieves 98% and 97% closed-loop success at a 10 Hz decision rate. Among successful trials, median fractions of decision steps requiring a new depth observation are only 0.9% and 1.3%, respectively, demonstrating high navigation success with substantially reduced observation demand. Real-world indoor flight experiments further validate navigation under intermittent depth observations, with all 10 trials successful.
