Autonomous Exploration and Mapping with RFS Occupancy-Grid SLAM

Ristic, B and Palmer, J 2018, 'Autonomous Exploration and Mapping with RFS Occupancy-Grid SLAM', Entropy, vol. 20, no. 6, pp. 1-8.

Document type: Journal Article
Collection: Journal Articles

Title Autonomous Exploration and Mapping with RFS Occupancy-Grid SLAM
Author(s) Ristic, B
Palmer, J
Year 2018
Journal name Entropy
Volume number 20
Issue number 6
Start page 1
End page 8
Total pages 8
Publisher MDPI
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.
Subject Signal Processing
Autonomous Vehicles
Keyword(s) localisation and mapping
particle filter
Rényi divergence
random finite sets
DOI - identifier 10.3390/e20060456
Copyright notice © 2018 by the authors. Licensee MDPI, Basel, Switzerland. Creative Commons Attribution (CC BY) license.
ISSN 1099-4300
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