AI 中文总结
针对模糊感知与定位漂移导致的流场映射幽灵结构问题，提出地图参考感知的保守融合框架，可显著降低幽灵污染并保留高地图覆盖率。
AI 中文摘要
移动机器人可通过机载感知推断局部流场结构，但局部合理的估计值未必适合写入全局地图。相似的流结构会产生模糊观测，而定位漂移会导致预测的斑块被写入错误位置，重复的未配准更新会累积成持久的幽灵结构。我们提出一种地图参考感知的保守融合框架来解决该失效模式：该模型预测局部速度斑块与学习到的安全写分数，该分数会持续衰减不确定的地图更新，同时在无可靠地图参考时允许初始化。在合成射流与横流环境中，所提方法相比无门控融合使平均幽灵污染降低42%；利用推进器尾迹的真实压力与光流测量进行零样本硬件重放，进一步使幽灵污染降低39%，同时保留81%的地图覆盖率。这些结果表明，安全地图写入对模糊感知与定位漂移下的流场映射至关重要。
英文摘要
Mobile robots can infer local flow structure from onboard sensing, but a locally plausible estimate is not always safe to write into a global map. Similar flow structures may produce ambiguous observations, while localization drift causes predicted patches to be written at incorrect locations. Repeated misregistered updates then accumulate into persistent ghost structures. We address this failure mode with a map-reference-aware conservative fusion framework. The model predicts a local velocity patch and a learned write-safety score that continuously attenuates uncertain map updates while permitting initialization when no reliable map reference is available. Across synthetic jet and crossflow environments, the proposed method reduces average ghost contamination by 42% relative to ungated fusion. A zero-shot hardware replay using real pressure and optical-flow measurements from a thruster wake further reduces ghost contamination by 39% while retaining 81% map coverage. These results show that safe map writing is critical for flow mapping under ambiguous sensing and localization drift.
