Self-supervised Vector-Quantization in Visual SLAM using Deep Convolutional Autoencoders

TL;DR

This paper introduces a self-supervised autoencoder-based vector quantization method for visual SLAM, improving loop closure detection accuracy and efficiency in large-scale environments by reducing labeling needs.

Contribution

It proposes a novel self-supervised autoencoder approach for vector quantization in visual SLAM, enhancing loop closure detection without extensive labeled data.

Findings

Autoencoder-based VQ outperforms traditional methods in accuracy.

The method reduces computational time and memory usage.

Enhanced SLAM performance in indoor and outdoor datasets.

Abstract

In this paper, we introduce AE-FABMAP, a new self-supervised bag of words-based SLAM method. We also present AE-ORB-SLAM, a modified version of the current state of the art BoW-based path planning algorithm. That is, we have used a deep convolutional autoencoder to find loop closures. In the context of bag of words visual SLAM, vector quantization (VQ) is considered as the most time-consuming part of the SLAM procedure, which is usually performed in the offline phase of the SLAM algorithm using unsupervised algorithms such as Kmeans++. We have addressed the loop closure detection part of the BoW-based SLAM methods in a self-supervised manner, by integrating an autoencoder for doing vector quantization. This approach can increase the accuracy of large-scale SLAM, where plenty of unlabeled data is available. The main advantage of using a self-supervised is that it can help reducing the amount of labeling. Furthermore, experiments show that autoencoders are far more efficient than semi-supervised methods like graph convolutional neural networks, in terms of speed and memory consumption. We integrated this method into the state of the art long range appearance based visual bag of word SLAM, FABMAP2, also in ORB-SLAM. Experiments demonstrate the superiority of this approach in indoor and outdoor datasets over regular FABMAP2 in all cases, and it achieves higher accuracy in loop closure detection and trajectory generation.