IEEE Robotics & Automation Magazine - June 2014 - 26
New Concepts in
Map Estimation
© artville
SLAM Gets
a PHD
By Martin Adams, Ba-Ngu Vo, Ronald Mahler,
and John Mullane
H
aving been referred to as the Holy Grail of autonomous robotics research, simultaneous localization and mapping (SLAM) lies at the core of most
the autonomous robotic applications [1]. This article explains the recent advances in the representations of robotic sensor measurements and the map itself as
well as their consequences on the robustness of SLAM. Fundamentally, the concept of a set-based measurement and map
state representation allows all of the measurement information, spatial and detection, to be incorporated into joint
Bayesian SLAM frameworks. Modeling measurements and
the map state as random finite sets (RFSs) rather than the traditionally adopted random vectors is not merely a triviality of
notation. It will be demonstrated that a set-based framework
Digital Object Identifier 10.1109/MRA.2014.2304111
Date of publication: 9 May 2014
26
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IEEE ROBOTICS & AUTOMATION MAGAZINE
*
June 2014
circumvents the necessity for any fragile data association and
map management heuristics, which are necessary in vectorbased solutions.
The article focuses on an implementation of the simplest
Bayesian set-based estimator-the probability hypothesis
density (PHD) filter-and its application to SLAM. Using
particles to represent hypothesized vehicle trajectories and
a Rao-Blackwellized (RB) PHD filter for each particle's
map, estimates of the location and, equally importantly, the
number of features that have passed through the field of
view (FoV) of a vehicle's sensor(s) are estimated.
Vector-based SLAM solutions do not estimate this number
of features and instead rely on map management and data association heuristics, which are implemented outside of the Bayesian (e.g., Kalman, unscented Kalman, particle filter) estimator to
determine which features to update. In the parallel but closely
related field of target tracking, recent research has shown that
1070-9932/14/$31.00©2014IEEE
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