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Autonomous Exploration and Mapping with RFS Occupancy-Grid SLAM.


ABSTRACT: 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.

SUBMITTER: Ristic B 

PROVIDER: S-EPMC7512974 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Autonomous Exploration and Mapping with RFS Occupancy-Grid SLAM.

Ristic Branko B   Palmer Jennifer L JL  

Entropy (Basel, Switzerland) 20180612 6


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  ...[more]

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