By Topic

RFS Collaborative Multivehicle SLAM: SLAM in Dynamic High-Clutter Environments

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Moratuwage, D. ; Sch. of EEE, Nanyang Technol. Univ., Singapore, Singapore ; Danwei Wang ; Rao, A. ; Senarathne, N.
more authors

Recently, we proposed a novel solution to the collaborative multivehicle simultaneous localization and mapping (CMSLAM) problem by extending the random finite set (RFS) SLAM filter framework using recently developed multisensor information fusion techniques in the finite set statistics. We modeled the measurements and the landmark map as RFSs, and a joint posterior consisting of the landmark map and the vehicle trajectories was propagated in time to solve the CMSLAM problem. The proposed solution is based on the Rao?Blackwellized particle filter-based vehicle trajectories posterior estimation and the probability hypothesis density (PHD) filter-based landmark map posterior estimation. In this article, we evaluate the performance of this solution under dynamic high-clutter environmental conditions using a series of simulations and an actual experiment.

Published in:

Robotics & Automation Magazine, IEEE  (Volume:21 ,  Issue: 2 )