PSGSL: A Probabilistic Framework Integrating Semantic Scene Understanding and Gas Sensing for Gas Source Localization

TL;DR

This paper introduces a probabilistic framework that combines semantic scene understanding with gas sensing to enhance the accuracy of gas source localization in robotics, addressing hardware limitations and leveraging multi-sensor data.

Contribution

It presents a novel probabilistic approach that integrates semantic knowledge into gas source localization, improving estimation accuracy by utilizing additional sensory information.

Findings

Semantic integration improves localization accuracy.

Framework adaptable to existing GSL algorithms.

Enhanced estimation robustness with semantic data.

Abstract

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.