A Kalman Filter Based Approach To Probabilistic Gas Distribution Mapping
2013 (English)Conference paper (Refereed)
Building a model of gas concentrations has important indus-trial and environmental applications, and mobile robots ontheir own or in cooperation with stationary sensors play animportant role in this task. Since an exact analytical de-scription of turbulent flow remains an intractable problem,we propose an approximate approach which not only esti-mates the concentrations but also their variances for eachlocation. Our point of view is that of sequential Bayesianestimation given a lattice of 2D cells treated as hidden vari-ables. We first discuss how a simple Kalman filter pro-vides a solution to the estimation problem. To overcomethe quadratic computational complexity with the mappedarea exhibited by a straighforward application of Kalmanfiltering, we introduce a sparse implementation which runsin constant time. Experimental results for a real robot vali-date the proposed method.
Place, publisher, year, edition, pages
2013. 217-222 p.
Kalman Filter, Gas Distribution Mapping, Mobile Olfaction
Research subject Computer Science
IdentifiersURN: urn:nbn:se:oru:diva-32672DOI: 10.1145/2480362.2480409ScopusID: eid=2-s2.0-84877944182ISBN: 978-1-4503-1656-9OAI: oai:DiVA.org:oru-32672DiVA: diva2:676862
28th ACM Symposium on Applied Computing (SAC 2013), Special Track on Intelligent Robotics Systems
© ACM, YYYY. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is published in 28th ACM Symposium on Applied Computing (SAC 2013), 2013} http://doi.acm.org/10.1145/2480362.2480409"2013-12-072013-12-072015-01-23Bibliographically approved