Autonomous Exploration and Mapping with RFS Occupancy-Grid SLAM

This short note addresses the problem of autonomous on-line path-panning for exploration and occupancy-grid mapping using a mobile robot. The underlying algorithm for simultaneous localisation and mapping (SLAM) is based on random-finite set (RFS) modelling of ranging sensor measurements, implemente...

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Bibliographic Details
Published inEntropy (Basel, Switzerland) Vol. 20; no. 6; p. 456
Main Authors Ristic, Branko, Palmer, Jennifer
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2018
MDPI
Subjects
Algorithms
Automation
Decision making
Divergence
Exploration
International conferences
localisation and mapping
Localization
Modelling
Motion control
particle filter
random finite sets
Robotics
Robots
Rényi divergence
Sensors
Simultaneous localization and mapping
Technical Note
Uncertainty
Australia
United States > US
Germany
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Summary:This short note addresses the problem of autonomous on-line path-panning for exploration and occupancy-grid mapping using a mobile robot. The underlying algorithm for simultaneous localisation and mapping (SLAM) is based on random-finite set (RFS) modelling of ranging sensor measurements, implemented as a Rao-Blackwellised particle filter. Path-planning in general must trade-off between exploration (which reduces the uncertainty in the map) and exploitation (which reduces the uncertainty in the robot pose). In this note we propose a reward function based on the Rényi divergence between the prior and the posterior densities, with RFS modelling of sensor measurements. This approach results in a joint map-pose uncertainty measure without a need to scale and tune their weights.
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ISSN:1099-4300
1099-4300
DOI:10.3390/e20060456