Efficient Decentralized Visual Place Recognition From Full-Image Descriptors
This work addresses scalability in multi-robot systems for visual place recognition, though it is incremental as it builds on prior decentralized methods.
The paper tackles the problem of decentralized visual place recognition by adapting a key-value lookup approach to full-image descriptors, resulting in a simpler system than previous methods but with limitations when deployment environments differ from training distributions.
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.