Please use this identifier to cite or link to this item:
Title: Extending Bayesian RFS SLAM to multi-vehicle SLAM
Authors: Moratuwage, Diluka
Vo, Ba-Ngu
Wang, Danwei
Wang, Han
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.
DOI: 10.1109/ICARCV.2012.6485232
Rights: © 2012 IEEE.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Conference Papers

Citations 20

Updated on Mar 6, 2021

Page view(s) 10

Updated on Sep 25, 2022

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.