用语义理解提升气体源定位精度，融合视觉等多源信息
PSGSL: A Probabilistic Framework Integrating Semantic Scene Understanding and Gas Sensing for Gas Source Localization
- 构建概率框架融合语义场景与气体传感数据
- 在复杂环境中显著提升气体源定位准确率
- 适合做智能机器人环境感知与定位的科研人员
语义场景理解使机器人能利用多源异构传感器信息进行复杂推理，从而执行更复杂的任务并获得更高精度的结果。然而，这种能力的提升带来了更高的计算与设计复杂度。为应对这一挑战，我们提出一种将语义知识融入气体源定位（GSL）过程的概率框架。该问题面临诸多未解挑战，且受传感硬件限制。通过引入语义理解，可利用视觉等额外信息改善源位置估计。我们展示了该框架如何适配已有GSL算法，并验证了引入语义数据对定位结果的显著改进作用。
原文摘要 · Abstract (English)
Semantic scene understanding allows a robotic agent to reason about problems in complex ways, using information from multiple and varied sensors to make deductions about a particular matter. As a result, this form of intelligent robotics is capable of performing more complex tasks and achieving more precise results than simpler approaches based on single data sources. However, these improved capabilities come at the cost of higher complexity, both computational and in terms of design. Due to the increased design complexity, formal approaches for exploiting semantic understanding become necessary. We present here a probabilistic formulation for integrating semantic knowledge into the process of gas source localization (GSL). The problem of GSL poses many unsolved challenges, and proposed solutions need to contend with the constraining limitations of sensing hardware. By exploiting semantic scene understanding, we can leverage other sources of information, such as vision, to improve the estimation of the source location. We show how our formulation can be applied to pre-existing GSL algorithms and the effect that including semantic data has on the produced estimations of the location of the source.
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