Title
Rao-Blackwellised PHD SLAM
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.
Year
DOI
Venue
2010
10.1109/ROBOT.2010.5509626
Robotics and Automation
Keywords
Field
DocType
Bayes methods,Gaussian distribution,SLAM (robots),particle filtering (numerical methods),probability,sensor fusion,Gaussian mixture probability hypothesis density filter,Rao-Blackwellised probability hypothesis density SLAM,data association ambiguity,data association uncertainty,feature map modeling,feature-based SLAM,joint posterior distribution,particle filter,probability hypothesis density-SLAM filter,random finite set,rigorous Bayesian formulation,set-valued map,tractable solution,vehicle trajectory
Pattern recognition,Clutter,Particle filter,Posterior probability,Sensor fusion,Gaussian,Artificial intelligence,Simultaneous localization and mapping,Mathematics,Trajectory,Bayesian probability
Conference
Volume
Issue
ISSN
2010
1
1050-4729 E-ISBN : 978-1-4244-5040-4
ISBN
Citations 
PageRank 
978-1-4244-5040-4
14
1.22
References 
Authors
8
3
Name
Order
Citations
PageRank
Mullane, J.11075.19
Ba-Ngu Vo245722.45
Martin David Adams3141.22