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| Type | Dissertation |
| Scope | Discipline-based scholarship |
| Title | Decentralized Multi-Agent Visual SLAM |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 179 |
| Date | 2021 |
| Abstract Text | Simultaneous Localization and Mapping (SLAM) is an algorithm which confers to agents a sense of the environment and how they move through it. Examples of such agents are autonomous robots, but also augmented- or virtual reality devices worn by humans. It allows them to build a map of the environment, to localize themselves within that map, and to plan routes between points of interest inside that map. Multi-agent SLAM extends this sense of the environment to a group of agents, allowing them to additionally profit from each others' knowledge of the environment. In this thesis, this knowledge is acquired using vision. Cameras provide rich information for various tasks and are compact, low-cost, and ubiquitous at the same time. The drawback of this rich information is that it would per default require a lot of bandwidth to transmit between agents. While new infrastructure provides more and more high-bandwidth wireless communication, it is far from covering all multi-agent SLAM application environments. It has trouble penetrating through rock, walls and water, and across large distances. Besides, saving bandwidth enables scalability to larger groups of agents, and leaves the bandwidth available to other uses. This thesis thus focuses particularly on multi-agent mapping using minimal data exchange. For similar reasons - scalability and bandwidth savings - the emphasis is put on developing a decentralized, as opposed to a centralized SLAM system. In the pursuit of such a system, three components of visual SLAM, where data is exchanged between the participating agents, emerge: place recognition, relative pose estimation, and map optimization. In this thesis, I mainly work on ways of achieving the former two with as little data exchange as possible. As for map optimization, I make the point that it is most likely not needed in most decentralized visual SLAM applications. Map optimization mitigates the global inconsistency that inevitably happens due to drift in visual odometry. In the past, however, it has been shown that global consistency is not necessary for navigation in the map. In my thesis, I show that it is not needed for exploration, either. The following is a list of contributions of this thesis, in chronological order: - A method for decentralized bag-of-words-based visual place recognition for a group of n agents which requires n times less data exchange than conventional decentralized place recognition. - A similar method for decentralized learned-embedding-based visual place recognition which requires even less data exchange. - A first truly data-efficient, full decentralized visual SLAM system based on state-of-the-art components, including the previous contribution, and additional methods for reducing the data exchange, without sacrificing the accuracy of the shared map. Ten robots operating for one minute only exchange 2MB of data in total for full multi-agent visual SLAM functionality. - A detector for visual features which is capable of extracting a minimal set of feature points that enables localization with as little as 50 visual features. - A completely new approach to feature detection and matching, where features that are implicitly matched between images are detected, thus rendering feature descriptors obsolete in the considered application case. - A data representation for exploration which enables exploration using a globally inconsistent state estimate. |
| Other Identification Number | merlin-id:21808 |
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