# Merging Classification Predictions with Sequential Information for Lightweight Visual Place Recognition in Changing Environments

arXiv: https://arxiv.org/abs/2210.00834 · HTML: https://mlsanity.com/paper/2210.00834/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 50 references that are in the map.
- First posted on arXiv 3 Oct 2022.

## Builds on (most cited first, 23 of 23)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [BinaryConnect: Training Deep Neural Networks with binary weights during propagations](https://mlsanity.com/paper/1511.00363/) (2015)
- [Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations](https://mlsanity.com/paper/1609.07061/) (2016)
- [TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation](https://mlsanity.com/paper/2103.10158/) (2021)
- [VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Change](https://mlsanity.com/paper/2005.08135/) (2020)
- [Deep Learning Features at Scale for Visual Place Recognition](https://mlsanity.com/paper/1701.05105/) (2017)
- [Where is your place, Visual Place Recognition?](https://mlsanity.com/paper/2103.06443/) (2021)
- [A Holistic Visual Place Recognition Approach using Lightweight CNNs for Significant ViewPoint and Appearance Changes](https://mlsanity.com/paper/1811.03032/) (2018)
- [Binary Ensemble Neural Network: More Bits per Network or More Networks per Bit?](https://mlsanity.com/paper/1806.07550/) (2018)
- [Multi-Process Fusion: Visual Place Recognition Using Multiple Image Processing Methods](https://mlsanity.com/paper/1903.03305/) (2019)
- [LoST? Appearance-Invariant Place Recognition for Opposite Viewpoints using Visual Semantics](https://mlsanity.com/paper/1804.05526/) (2018)
- [Lightweight Unsupervised Deep Loop Closure](https://mlsanity.com/paper/1805.07703/) (2018)
- [A Cross-Season Correspondence Dataset for Robust Semantic Segmentation](https://mlsanity.com/paper/1903.06916/) (2019)
- [Hierarchical Multi-Process Fusion for Visual Place Recognition](https://mlsanity.com/paper/2002.03895/) (2020)
- [Binary Neural Networks for Memory-Efficient and Effective Visual Place Recognition in Changing Environments](https://mlsanity.com/paper/2010.00716/) (2020)
- [A Hybrid Compact Neural Architecture for Visual Place Recognition](https://mlsanity.com/paper/1910.06840/) (2019)
- [Improving Visual Place Recognition Performance by Maximising Complementarity](https://mlsanity.com/paper/2102.08416/) (2021)
- [What makes visual place recognition easy or hard?](https://mlsanity.com/paper/2106.12671/) (2021)
- [Convolutional Neural Network-Based Image Representation for Visual Loop Closure Detection](https://mlsanity.com/paper/1504.05241/) (2015)
- [An Efficient and Scalable Collection of Fly-inspired Voting Units for Visual Place Recognition in Changing Environments](https://mlsanity.com/paper/2109.10986/) (2021)
- [Visual Place Recognition for Aerial Robotics: Exploring Accuracy-Computation Trade-off for Local Image Descriptors](https://mlsanity.com/paper/1908.00258/) (2019)
- [Unsupervised Complementary-aware Multi-process Fusion for Visual Place Recognition](https://mlsanity.com/paper/2112.04701/) (2021)

## Abstract (arXiv)

Low-overhead visual place recognition (VPR) is a highly active research topic. Mobile robotics applications often operate under low-end hardware, and even more hardware capable systems can still benefit from freeing up onboard system resources for other navigation tasks. This work addresses lightweight VPR by proposing a novel system based on the combination of binary-weighted classifier networks with a one-dimensional convolutional network, dubbed merger. Recent work in fusing multiple VPR techniques has mainly focused on increasing VPR performance, with computational efficiency not being highly prioritized. In contrast, we design our technique prioritizing low inference times, taking inspiration from the machine learning literature where the efficient combination of classifiers is a heavily researched topic. Our experiments show that the merger achieves inference times as low as 1 millisecond, being significantly faster than other well-established lightweight VPR techniques, while achieving comparable or superior VPR performance on several visual changes such as seasonal variations and viewpoint lateral shifts.