Structurally aware 3D gas distribution mapping using belief propagation: a real-time algorithm for robotic deployment

TL;DR

This paper introduces a real-time 3D gas mapping algorithm using Gaussian belief propagation that accounts for obstacles, enabling autonomous robots to quickly generate detailed gas distribution maps in complex environments.

Contribution

It presents the first real-time 3D gas mapping method based on belief propagation that incorporates obstacle information and is deployable onboard robots.

Findings

Outperforms existing methods in speed by orders of magnitude.

Successfully deployed onboard a ground robot in real-world scenarios.

Provides high-resolution 3D gas distribution maps in real time.

Abstract

This paper proposes a new 3D gas distribution mapping technique based on the local message passing of Gaussian belief propagation that is capable of resolving in real time, concentration estimates in 3D space whilst accounting for the obstacle information within the scenario, the first of its kind in the literature. The gas mapping problem is formulated as a 3D factor graph of Gaussian potentials, the connections of which are conditioned on local occupancy values. The Gaussian belief propagation framework is introduced as the solver and a new hybrid message scheduler is introduced to increase the rate of convergence. The factor graph problem is then redesigned as a dynamically expanding inference task, coupling the information of consecutive gas measurements with local spatial structure obtained by the robot. The proposed algorithm is compared to the state of the art methods in 2D and 3D simulations and is found to resolve distribution maps orders of magnitude quicker than typical direct solvers. The proposed framework is then deployed for the first time onboard a ground robot in a 3D mapping and exploration task. The system is shown to be able to resolve multiple sensor inputs and output high resolution 3D gas distribution maps in a GPS denied cluttered scenario in real time. This online inference of complicated plume structures provides a new layer of contextual information over its 2D counterparts and enables autonomous systems to take advantage of real time estimates to inform potential next best sampling locations.