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|Title:||Extending Bayesian RFS SLAM to multi-vehicle SLAM||Authors:||Moratuwage, Diluka
|Keywords:||DRNTU::Engineering::Electrical and electronic engineering||Issue Date:||2012||Source:||Moratuwage, D., Vo, B.-N., Wang, D., & Wang, H. (2012). Extending Bayesian RFS SLAM to multi-vehicle SLAM. 2012 12th International Conference on Control Automation Robotics & Vision (ICARCV), 638-643.||Abstract:||In this paper we present a novel solution to the Multi-Vehicle SLAM (MVSLAM) problem by extending the random finite set (RFS) based SLAM filter framework using two recently developed multi-sensor information fusion approaches. Our solution is based on the modelling of the measurements and the landmark map as RFSs and factorizing the MVSLAM posterior into a product of the joint vehicle trajectories posterior and the landmark map posterior conditioned the vehicle trajectories. The joint vehicle trajectories posterior is propagated using a particle filter while the landmark map posterior conditioned on the vehicle trajectories is propagated using a Gaussian Mixture (GM) implementation of the probability hypothesis density (PHD) filter.||URI:||https://hdl.handle.net/10356/96989
|DOI:||10.1109/ICARCV.2012.6485232||Rights:||© 2012 IEEE.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||EEE Conference Papers|
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