Publication:Efficient Decentralized Visual Place Recognition From Full-Image Descriptors

• Presented at: 1st International Symposium on Multi-Robot and Multi-Agent Systems, University of Southern California
• Publication date: 2017

In this paper, we discuss the adaptation of our decentralized place recognition method described in [1] to fullimage 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 bagof-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.

Reference

• Detailed record: https://infoscience.epfl.ch/record/232969?ln=en
• EPFL-CONF-232969

• URL: https://arxiv.org/pdf/1705.10739.pdf