Recursive Bayesian Initialization of Localization Based on Ranging and Dead Reckoning
2013 (English)In: Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on, IEEE conference proceedings, 2013, 1399-1404 p.Conference paper (Refereed)
The initialization of the state estimation in a localization scenario based on ranging and dead reckoning is studied. Specifically, we treat a cooperative localization setup and consider the problem of recursively arriving at a unimodal state estimate with sufficiently low covariance such that covariance based filters can be used to estimate an agent's state subsequently. The initialization of the position of an anchor node will be a special case of this. A number of simplifications/assumptions are made such that the estimation problem can be seen as that of estimating the initial agent state given a deterministic surrounding and dead reckoning. This problem is solved by means of a particle filter and it is described how continual states and covariance estimates are derived from the solution. Finally, simulations are used to illustrate the characteristics of the method and experimental data are briefly presented.
Place, publisher, year, edition, pages
IEEE conference proceedings, 2013. 1399-1404 p.
, IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
A-particles, Anchor nodes, Bayesian, Cooperative localization, Dead reckoning, Estimation problem, Scenario-based, State estimates
Computer Vision and Robotics (Autonomous Systems) Signal Processing
IdentifiersURN: urn:nbn:se:kth:diva-133471DOI: 10.1109/IROS.2013.6696532ISI: 000331367401074ScopusID: 2-s2.0-84893755843ISBN: 978-146736358-7OAI: oai:DiVA.org:kth-133471DiVA: diva2:661828
IEEE/RSJ International Conference on Intelligent Robots and Systems, November 3-8, 2013 at Tokyo Big Sight, Japan
QC 201401102013-11-052013-11-052014-04-10Bibliographically approved