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Efficient decentralized visual place recognition using a distributed inverted index


Cieslewski, Titus; Scaramuzza, Davide (2017). Efficient decentralized visual place recognition using a distributed inverted index. IEEE Robotics and Automation Letters, 2(2):640-647.

Abstract

State-of-the-art systems that place recognition in a group of n robots either rely on a centralized solution, where each robot's map is sent to a central server, or a decentralized solution, where the map is either sent to all other robots, or robots within a communication range. Both approaches have their drawbacks: centralized systems rely on a central entity, which handles all the computational load and cannot be deployed in large, remote areas, whereas decentralized systems either exchange n times more data or preclude matches between robots that visit the same place at different times while never being close enough to communicate directly. We propose a novel decentralized approach, which requires a similar amount of data exchange as a centralized system, without precluding any matches. The core idea is that the candidate selection in visual bag-of-words can be distributed by preassigning words of the vocabulary to different robots. The result of this candidate selection is then used to choose a single robot to which the full query is sent. We validate our approach on real data and discuss its merit in different network models. To the best of our knowledge, this is the first work to use a distributed inverted index in multirobot place recognition.

Abstract

State-of-the-art systems that place recognition in a group of n robots either rely on a centralized solution, where each robot's map is sent to a central server, or a decentralized solution, where the map is either sent to all other robots, or robots within a communication range. Both approaches have their drawbacks: centralized systems rely on a central entity, which handles all the computational load and cannot be deployed in large, remote areas, whereas decentralized systems either exchange n times more data or preclude matches between robots that visit the same place at different times while never being close enough to communicate directly. We propose a novel decentralized approach, which requires a similar amount of data exchange as a centralized system, without precluding any matches. The core idea is that the candidate selection in visual bag-of-words can be distributed by preassigning words of the vocabulary to different robots. The result of this candidate selection is then used to choose a single robot to which the full query is sent. We validate our approach on real data and discuss its merit in different network models. To the best of our knowledge, this is the first work to use a distributed inverted index in multirobot place recognition.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Date:1 April 2017
Deposited On:22 Aug 2017 12:40
Last Modified:22 Aug 2017 12:40
Publisher:Institute of Electrical and Electronics Engineers
ISSN:2377-3766
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1109/lra.2017.2650153
Official URL:http://rpg.ifi.uzh.ch/docs/RAL16_Cieslewski.pdf
Other Identification Number:merlin-id:15099

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