基于射频线索的导航：多径不确定性下的具身感知行动
Navigation with RF Cues: Embodied Perception Action under Multipath Uncertainty
- Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对无地图智能工厂巡检，提出基于射频与视觉融合的不确定性感知导航框架，构建Habitat Sionna RT基准，在未见场景中SR提升18.2%、SPL提升11.5%。
AI中文摘要：
智能工厂巡检要求机器人在没有预先地图或已知目标坐标的情况下到达连接的设备。当遮挡或光线不足限制目标检测时，来自目标的射频（RF）信号可提供方向线索以补充视觉观察。然而，多径传播会扭曲这些线索，使得从瞬时射频测量中推断目标的真实方向变得困难。为了在这些条件下开展导航研究，我们首先构建了一个Habitat Sionna RT基准，利用详细的场景几何和指定的材料属性，根据机器人动作生成对齐的视觉和射频观测。基于该基准，我们提出了一种不确定性感知的多模态导航框架，该框架从射频、视觉和位姿观测的历史中联合估计目标方向及其不确定性。这些估计与视觉上下文一起指导动作选择。在未见场景中的实验显示，与最强评估基线相比，成功率（SR）相对提高了18.2%，按路径长度加权的成功率（SPL）相对提高了11.5%。
英文摘要：
Smart factory inspection requires robots to reach connected equipment without a prior map or known target coordinates. Radio frequency (RF) signals from the target can provide directional cues to complement visual observations when occlusion or poor lighting limits target detection. However, multipath propagation can distort these cues, making it difficult to infer the target's true direction from instantaneous RF measurements. To enable navigation research under these conditions, we first construct a Habitat Sionna RT benchmark that uses detailed scene geometry and assigned material properties to generate aligned visual and RF observations in response to robot actions. Building on this benchmark, we propose an uncertainty aware multimodal navigation framework that jointly estimates target direction and its uncertainty from a history of RF, visual, and pose observations. These estimates inform action selection alongside visual context. Experiments in unseen scenes show relative improvements of 18.2% in success rate (SR) and 11.5% in success weighted by path length (SPL) over the strongest evaluated baseline.
