Building gas concentration gridmaps with a mobile robot

Abstract

This paper addresses the problem of mapping the structure of a gas distribution by creating concentration gridmaps from the data collected by a mobile robot equipped with gas sensors. By contrast to metric gridmaps extracted from sonar or laser range scans, a single measurement from a gas sensor provides information about a comparatively small area. To overcome this problem, a mapping technique is introduced that uses a Gaussian weighting function to model the decreasing likelihood that a particular reading represents the true concentration with respect to the distance from the point of measurement. This method is evaluated in terms of its suitability regarding the slow response and recovery of the gas sensors, and experimental comparisons of different exploration strategies are presented. The stability of the mapped structures and the capability to use concentration gridmaps to locate a gas source are also discussed.

Automated mobile sensing: Towards high-granularity agile indoor environmental quality monitoring

“…While the results are comprehensive, the carts often require considerable labor cost and manual navigation. Several studies exist to deploy robots for monitoring and identifying pollutants both indoor and outdoor [38,39,40,41]; however, the methods do not distinguish the global trend of physical parameters from their local variations, which might lower the estimation accuracy, and the results have not been validated against a ground truth, which requires a dense sensor network for comparison.…”

Multi-robot olfactory search in structured environments

“…Most of the related works concerning olfactory search have focused on either single robot experiments [1,2,3] or multiple robots operating in open areas free of obstacles [4,5,6] with a background fluid flow. The odor is carried downwind originating from the source forming a plume.…”

Three-Dimensional Statistical Gas Distribution Mapping in an Uncontrolled Indoor Environment

“…Gas distribution mapping with mobile gas sensors has been implemented and investigated by mobile robots equipped with an "e-nose" [8,9,10,11,12]. These approaches can be divided into two groups.…”

“…Model-based approaches such as the one proposed by Ishida et al [8] assume a particular model of the time-averaged gas distribution and estimate the corresponding parameters. Model-or parameter-free approaches deal with the fluctuating nature of the gas distribution either by recording individual concentration samples over a prolonged time (several minutes) [11,12] or by statistically integrating subsequent measurements into a spatial grid [9,10].…”