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|Title:||Rao-Blackwellised PHD SLAM||Authors:||Mullane, John
Adams, Martin David
|Keywords:||DRNTU::Engineering::Electrical and electronic engineering||Issue Date:||2010||Source:||Mullane, J., Vo, B. N., & Adams, M. D. (2010). Rao-Blackwellised PHD SLAM. IEEE International Conference on Robotics and Automation, 5410-5416.||Abstract:||This paper proposes a tractable solution to feature-based (FB) SLAM in the presence of data association uncertainty and uncertainty in the number of features. By modeling the feature map as a random finite set (RFS), a rigorous Bayesian formulation of the FB-SLAM problem that accounts for uncertainty in the number of features and data association is presented. As such, the joint posterior distribution of the set-valued map and vehicle trajectory is propagated forward in time as measurements arrive. A first order solution, coined the PHD-SLAM filter, is derived, which jointly propagates the posterior PHD or intensity function of the map and the posterior distribution of the trajectory of the vehicle. A Rao-Blackwellised implementation of the PHD-SLAM filter is proposed based on the Gaussian mixture PHD filter for the map and a particle filter for the vehicle trajectory. Simulated results demonstrate the merits of the proposed approach, particularly in situations of high clutter and data association ambiguity.||URI:||https://hdl.handle.net/10356/90756
|ISSN:||1050-4729||DOI:||10.1109/ROBOT.2010.5509626||Rights:||© 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [DOI: http://dx.doi.org/10.1109/ROBOT.2010.5509626].||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Conference Papers|
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