# What makes visual place recognition easy or hard?

arXiv: https://arxiv.org/abs/2106.12671 · HTML: https://mlsanity.com/paper/2106.12671/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 90 references that are in the map.
- First posted on arXiv 23 Jun 2021.

## Builds on (most cited first, 15 of 15)

- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [D2-Net: A Trainable CNN for Joint Detection and Description of Local Features](https://mlsanity.com/paper/1905.03561/) (2019)
- [Large-Scale Image Retrieval with Attentive Deep Local Features](https://mlsanity.com/paper/1612.06321/) (2016)
- [On the Performance of ConvNet Features for Place Recognition](https://mlsanity.com/paper/1501.04158/) (2015)
- [Night-to-Day Image Translation for Retrieval-based Localization](https://mlsanity.com/paper/1809.09767/) (2018)
- [Approximate Nearest Neighbor Search on High Dimensional Data --- Experiments, Analyses, and Improvement (v1.0)](https://mlsanity.com/paper/1610.02455/) (2016)
- [ComboGAN: Unrestrained Scalability for Image Domain Translation](https://mlsanity.com/paper/1712.06909/) (2017)
- [Hyperdimensional computing as a framework for systematic aggregation of image descriptors](https://mlsanity.com/paper/2101.07720/) (2021)
- [Delta Descriptors: Change-Based Place Representation for Robust Visual Localization](https://mlsanity.com/paper/2006.05700/) (2020)
- [Unsupervised Learning Methods for Visual Place Recognition in Discretely and Continuously Changing Environments](https://mlsanity.com/paper/2001.08960/) (2020)
- [Beyond ANN: Exploiting Structural Knowledge for Efficient Place Recognition](https://mlsanity.com/paper/2103.08366/) (2021)
- [Visual Localization Using Semantic Segmentation and Depth Prediction](https://mlsanity.com/paper/2005.11922/) (2020)
- [Segway DRIVE Benchmark: Place Recognition and SLAM Data Collected by A Fleet of Delivery Robots](https://mlsanity.com/paper/1907.03424/) (2019)

## Built on by (most cited first, 14 of 14)

- [Visual Place Recognition: A Tutorial](https://mlsanity.com/paper/2303.03281/) (2023)
- [Learning Sequential Descriptors for Sequence-based Visual Place Recognition](https://mlsanity.com/paper/2207.03868/) (2022)
- [Spiking Neural Networks for Visual Place Recognition via Weighted Neuronal Assignments](https://mlsanity.com/paper/2109.06452/) (2021)
- [Applications of Spiking Neural Networks in Visual Place Recognition](https://mlsanity.com/paper/2311.13186/) (2023)
- [JIST: Joint Image and Sequence Training for Sequential Visual Place Recognition](https://mlsanity.com/paper/2403.19787/) (2024)
- [Improving Visual Place Recognition Based Robot Navigation By Verifying Localization Estimates](https://mlsanity.com/paper/2407.08162/) (2024)
- [Unsupervised Complementary-aware Multi-process Fusion for Visual Place Recognition](https://mlsanity.com/paper/2112.04701/) (2021)
- [Unsupervised Quality Prediction for Improved Single-Frame and Weighted Sequential Visual Place Recognition](https://mlsanity.com/paper/2307.01464/) (2023)
- [Boosting Performance of a Baseline Visual Place Recognition Technique by Predicting the Maximally Complementary Technique](https://mlsanity.com/paper/2210.07509/) (2022)
- [Dynamically Modulating Visual Place Recognition Sequence Length For Minimum Acceptable Performance Scenarios](https://mlsanity.com/paper/2407.00863/) (2024)
- [Quantile Transfer for Reliable Operating Point Selection in Visual Place Recognition](https://mlsanity.com/paper/2602.04401/) (2026)
- [Merging Classification Predictions with Sequential Information for Lightweight Visual Place Recognition in Changing Environments](https://mlsanity.com/paper/2210.00834/) (2022)
- [NocPlace: Nocturnal Visual Place Recognition via Generative and Inherited Knowledge Transfer](https://mlsanity.com/paper/2402.17159/) (2024)
- [Breaking D'ej`a Vu: Independent Auditing of Visual Place Recognition through Vision-Language Reasoning](https://mlsanity.com/paper/2607.12818/) (2026)

## Abstract (arXiv)

Visual place recognition is a fundamental capability for the localization of mobile robots. It places image retrieval in the practical context of physical agents operating in a physical world. It is an active field of research and many different approaches have been proposed and evaluated in many different experiments. In the following, we argue that due to variations of this practical context and individual design decisions, place recognition experiments are barely comparable across different papers and that there is a variety of properties that can change from one experiment to another. We provide an extensive list of such properties and give examples how they can be used to setup a place recognition experiment easier or harder. This might be interesting for different involved parties: (1) people who just want to select a place recognition approach that is suitable for the properties of their particular task at hand, (2) researchers that look for open research questions and are interested in particularly difficult instances, (3) authors that want to create reproducible papers on this topic, and (4) also reviewers that have the task to identify potential problems in papers under review.