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

2025-01-22

AI Summary

To address low accuracy and poor robustness in gas source localization (GSL) within complex environments, this paper proposes the first unified probabilistic framework integrating visual semantic scene understanding with gas sensing. Methodologically, structured semantic segmentation outputs are converted into spatial priors and embedded into a Bayesian inference process based on a probabilistic graphical model, jointly modeling physical constraints of gas dispersion and visually derived semantic maps for cross-modal (vision + olfaction) cooperative estimation. The key contribution lies in the systematic incorporation of semantic scene understanding into probabilistic GSL modeling—marking the first such effort to overcome limitations of unimodal perception. Experiments demonstrate a 37% average reduction in localization error across both real-world and simulated scenarios, significantly improving estimation accuracy under sparse gas-sensing conditions. Moreover, the framework is compatible with mainstream GSL algorithms, empirically validating the effectiveness of semantic priors as an enhancement mechanism.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Reasoning under Uncertainty: Relational Probabilistic Models
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

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.

Problem

• Scene Understanding
• Gas Sensing
• Mathematical Model

Innovation

• PSGSL Model
• Semantic Understanding
• Multi-Sensor Integration