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arXiv · 1705.10739

Efficient Decentralized Visual Place Recognition From Full-Image Descriptors

Titus Cieslewski·Davide Scaramuzza
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Abstract

In this paper, we discuss the adaptation of our decentralized place recognition method described in [1] to full image descriptors. As we had shown, the key to making a scalable decentralized visual place recognition lies in exploting deterministic key assignment in a distributed key-value map. Through this, it is possible to reduce bandwidth by up to a factor of n, the robot count, by casting visual place recognition to a key-value lookup problem. In [1], we exploited this for the bag-of-words method [3], [4]. Our method of casting bag-of-words, however, results in a complex decentralized system, which has inherently worse recall than its centralized counterpart. In this paper, we instead start from the recent full-image description method NetVLAD [5]. As we show, casting this to a key-value lookup problem can be achieved with k-means clustering, and results in a much simpler system than [1]. The resulting system still has some flaws, albeit of a completely different nature: it suffers when the environment seen during deployment lies in a different distribution in feature space than the environment seen during training.

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Titus Cieslewski, Davide Scaramuzza. 2017-05-30. Efficient Decentralized Visual Place Recognition From Full-Image Descriptors. https://doi.org/10.1109/mrs.2017.8250934

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