Efficient Decentralized Visual Place Recognition Using a Distributed Inverted Index

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.