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Autonomous Robots
Erschienen in: Autonomous Robots 3/2014

01.03.2014

Anytime merging of appearance-based maps

verfasst von: Gorkem Erinc, Stefano Carpin

Erschienen in: Autonomous Robots | Ausgabe 3/2014

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Abstract

We consider the problem of merging together multiple appearance-based maps independently built by a team of robots jointly exploring an indoor environment. Due to the lack of accepted metrics to evaluate the quality of merged appearance-based maps, we propose to use algebraic connectivity for this purpose, and we discuss why this is an appropriate measure. Next, we introduce QuickConnect, an anytime algorithm aiming to maximize the given metric and we show how it can merge couple of maps, as well as multiple maps at the same time. The proposed algorithm has been implemented and tested on a fully functioning robotic system building appearance-based maps using a bag of words approach. QuickConnect outperforms alternative methods and features a convenient tradeoff between accuracy and speed.
Vorheriger Artikel A saliency-driven robotic head with bio-inspired saccadic behaviors for social robotics
Nächster Artikel Optimal coverage trajectories for a UGV with tradeoffs for energy and time

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Fußnoten
1
From now on we will refer to image-based maps in which no metric information is used as appearance-based maps.
 
2
Throughout the paper, vertex and image will be used interchangeably.
 
3
Kd-trees provide no speedup over exhaustive search for spaces with 10 or more dimensions for exact solutions, therefore striving for real time performance we decided for an approximate solution.
 
4
All code and dataset is freely available on our website.
 
5
It is known that \(2\delta (G)-n+2 \le \alpha (G)\), where \(n\) is the number of vertices (Fiedler 1973).
 
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Metadaten
Titel
Anytime merging of appearance-based maps
verfasst von
Gorkem Erinc
Stefano Carpin
Publikationsdatum
01.03.2014
Verlag
Springer US
Erschienen in
Autonomous Robots / Ausgabe 3/2014
Print ISSN: 0929-5593
Elektronische ISSN: 1573-7527
DOI
https://doi.org/10.1007/s10514-013-9352-1

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