IEEE Robotics & Automation Magazine - June 2014 - 53
By Diluka Moratuwage, Danwei Wang, Akshay Rao,
Namal Senarathne, and Han Wang
SLAM in Dynamic
High-Clutter Environments
RFS Collaborative
Multivehicle SLAM
R
Image lIcensed by Ingram PublIshIng
Digital Object Identifier 10.1109/MRA.2014.2312841
Date of publication: 12 May 2014
1070-9932/14/$31.00©2014IEEE
ecently, 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.
Almost all CMSLAM solutions in the literature are formulated using
conventional vector-based SLAM solutions, such as extended Kalman
filter-based SLAM [1], FastSLAM [2], and sparse extended information
filter-based SLAM [3]. As a result, all such CMSLAM solutions require
solving data association, clutter filtering, and map management as separate subalgorithms in addition to the complex fusion methods required
in multivehicle solutions.
To address such issues, Mullane et al. [4], [5] adopted RFS modeling into
SLAM by representing the measurements and the landmark map as RFSs
and appropriately modeling the measurements and the map transition
using finite set statistics. This solution addressed the map management,
June 2014
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