# Efficient Decentralized Visual Place Recognition From Full-Image Descriptors

## TL;DR

This paper presents a simplified decentralized visual place recognition system using full-image descriptors like NetVLAD, reducing bandwidth and complexity compared to previous bag-of-words approaches, but with limitations in cross-environment generalization.

## Contribution

It introduces a new decentralized place recognition method based on NetVLAD descriptors and k-means clustering, simplifying the system and improving scalability.

## Key findings

- Reduces bandwidth by a factor of n with deterministic key assignment.
- Simpler system compared to previous bag-of-words based methods.
- Performance drops when deployment environment differs from training environment.

## 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.