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

arXiv: https://arxiv.org/abs/2010.00716 · HTML: https://mlsanity.com/paper/2010.00716/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2020.

## Built on by (most cited first, 16 of 16)

- [General Place Recognition Survey: Towards Real-World Autonomy](https://mlsanity.com/paper/2405.04812/) (2024)
- [Design Space Exploration of Low-Bit Quantized Neural Networks for Visual Place Recognition](https://mlsanity.com/paper/2312.09028/) (2023)
- [Highly-Efficient Binary Neural Networks for Visual Place Recognition](https://mlsanity.com/paper/2202.12375/) (2022)
- [Sequence-Based Filtering for Visual Route-Based Navigation: Analysing the Benefits, Trade-offs and Design Choices](https://mlsanity.com/paper/2103.01994/) (2021)
- [Memory-Constrained Semantic Segmentation for Ultra-High Resolution UAV Imagery](https://mlsanity.com/paper/2310.04721/) (2023)
- [Structured Pruning for Efficient Visual Place Recognition](https://mlsanity.com/paper/2409.07834/) (2024)
- [On Motion Blur and Deblurring in Visual Place Recognition](https://mlsanity.com/paper/2412.07751/) (2024)
- [PROM: Prioritize Reduction of Multiplications Over Lower Bit-Widths for Efficient CNNs](https://mlsanity.com/paper/2505.03254/) (2025)
- [Data Efficient Visual Place Recognition Using Extremely JPEG-Compressed Images](https://mlsanity.com/paper/2209.08343/) (2022)
- [Data-Efficient Sequence-Based Visual Place Recognition with Highly Compressed JPEG Images](https://mlsanity.com/paper/2302.13314/) (2023)
- [TeTRA-VPR: A Ternary Transformer Approach for Compact Visual Place Recognition](https://mlsanity.com/paper/2503.02511/) (2025)
- [Merging Classification Predictions with Sequential Information for Lightweight Visual Place Recognition in Changing Environments](https://mlsanity.com/paper/2210.00834/) (2022)
- [Distillation Improves Visual Place Recognition for Low Quality Images](https://mlsanity.com/paper/2310.06906/) (2023)
- [TAT-VPR: Ternary Adaptive Transformer for Dynamic and Efficient Visual Place Recognition](https://mlsanity.com/paper/2505.16447/) (2025)
- [Are All Tokens Necessary for Visual Place Recognition? An Empirical Study of Token Reduction for Efficient Inference](https://mlsanity.com/paper/2607.15563/) (2026)
- [Removing the Compute Bottleneck for Event-based Visual Place Recognition with Spiking Neural Networks](https://mlsanity.com/paper/2610.16022/) (2026)

## Abstract (arXiv)

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.