IEEE Robotics & Automation Magazine - December 2012 - 105

CALL	
  FOR	
  PAPERS	
  
	
  

Special	
  Issue	
  on	
  	
  
Stochastic	
  Geometry	
  in	
  
Autonomous	
  Robotics	
  
Guest	
  Editors:	
  Martin	
  Adams,	
  Ba--‐Ngu	
  Vo,	
  and	
  Ronald	
  P.	
  Mahler	
  
Deadline	
  for	
  paper	
  submission:	
  10	
  January	
  2013	
  

Introduction: The robust interpretation of an autonomous vehicle's environment, as well as its
own position in that environment, underlies almost all autonomous robotic applications. This
Simultaneous Localization And Map building (SLAM) problem requires a robust representation
of the vehicle's surroundings (the map) in the presence of sensing/feature detection
uncertainties such as false positives, missed detections and spatial errors. Significant research
activity now exists in representing both measurements and the feature based SLAM map as a
Random Finite Set (RFS), rather than the conventionally used random vector. This is not
merely a triviality of representation. Recent research has shown that Finite Set Statistics
(FISST), developed for data fusion and estimation with RFSs, when applied to sensor
representations and SLAM, can eliminate the necessity of fragile map management and feature
association algorithms. Implementations in the form of the Probability Hypothesis Density
(PHD), Cardinalized PHD (C-PHD) and Multi-Target, Multi-Bernoulli (MeMBer) filters have
already demonstrated robust means of representing uncertain sensor data and maps, with the
unique abilities of jointly tracking both object spatial and existence uncertainties. The RFS map
concept therefore provides a robust paradigm under which the true number of features, which
have entered the field(s) of view of an autonomous vehicle's sensor(s), as well as their
locations, can be jointly estimated in a Bayes optimal manner, while taking into account feature
detection and false alarm probabilities.
Scope, Description and More Information: This special issue calls for magazine style articles
on the direct application of FISST to the issues of autonomous robotic sensing, mapping and
navigation with diverse sensing techniques and in various environments. The robustness of the
solutions presented should be demonstrated in the presence of sensing and sensor processing
uncertainty. Comparisons with conventional vector based techniques are also encouraged.
Topics of interest include, but are not limited to,
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Use of Sets for Sensor and Map Representations,
PHD Smoothing and Filtering Applications in Robotics,
Sensing, Mapping and SLAM in high clutter levels,
Multi-Vehicle SLAM,
Jointly incorporating object existence and spatial uncertainties into mapping and SLAM
Metrics to determine full mapping and SLAM errors
Detecting and Tracking Extended Targets

Complete details at http://www.ieee-ras.org/ram/specialissues.html
	
  

Digital Object Identifier 10.1109/MRA.2012.2221280

DECEMBER 2012

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IEEE ROBOTICS & AUTOMATION MAGAZINE

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105
http://www.ieee-ras.org/ram/specialissues.html
Table of Contents for the Digital Edition of IEEE Robotics & Automation Magazine - December 2012
