Binary Neural Networks for Memory-Efficient and Effective Visual Place Recognition in Changing Environments

TL;DR

This paper introduces FloppyNet, a binary neural network for visual place recognition that maintains high accuracy under environmental changes while drastically reducing memory usage and increasing speed, suitable for resource-limited robots.

Contribution

It is the first to apply binary neural networks to visual place recognition, achieving high performance with minimal resource consumption.

Findings

FloppyNet achieves comparable accuracy to full-precision models.

It reduces memory usage by 99%.

Inference speed increases sevenfold.

Abstract

Visual place recognition (VPR) is a robot's ability to determine whether a place was visited before using visual data. While conventional hand-crafted methods for VPR fail under extreme environmental appearance changes, those based on convolutional neural networks (CNNs) achieve state-of-the-art performance but result in heavy runtime processes and model sizes that demand a large amount of memory. Hence, CNN-based approaches are unsuitable for resource-constrained platforms, such as small robots and drones. In this paper, we take a multi-step approach of decreasing the precision of model parameters, combining it with network depth reduction and fewer neurons in the classifier stage to propose a new class of highly compact models that drastically reduces the memory requirements and computational effort while maintaining state-of-the-art VPR performance. To the best of our knowledge, this is the first attempt to propose binary neural networks for solving the visual place recognition problem effectively under changing conditions and with significantly reduced resource requirements. Our best-performing binary neural network, dubbed FloppyNet, achieves comparable VPR performance when considered against its full-precision and deeper counterparts while consuming 99% less memory and increasing the inference speed seven times.