# SwitchHit: A Probabilistic, Complementarity-Based Switching System for Improved Visual Place Recognition in Changing Environments

arXiv: https://arxiv.org/abs/2203.00591 · HTML: https://mlsanity.com/paper/2203.00591/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 32 references that are in the map.
- First posted on arXiv 1 Mar 2022.

## Builds on (most cited first, 14 of 14)

- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [Particular object retrieval with integral max-pooling of CNN activations](https://mlsanity.com/paper/1511.05879/) (2015)
- [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)
- [On the Performance of ConvNet Features for Place Recognition](https://mlsanity.com/paper/1501.04158/) (2015)
- [A Holistic Visual Place Recognition Approach using Lightweight CNNs for Significant ViewPoint and Appearance Changes](https://mlsanity.com/paper/1811.03032/) (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)
- [Memorable Maps: A Framework for Re-defining Places in Visual Place Recognition](https://mlsanity.com/paper/1811.03529/) (2018)
- [Improving Visual Place Recognition Performance by Maximising Complementarity](https://mlsanity.com/paper/2102.08416/) (2021)
- [VIZARD: Reliable Visual Localization for Autonomous Vehicles in Urban Outdoor Environments](https://mlsanity.com/paper/1902.04343/) (2019)

## Built on by (most cited first, 8 of 8)

- [A-MuSIC: An Adaptive Ensemble System For Visual Place Recognition In Changing Environments](https://mlsanity.com/paper/2303.14247/) (2023)
- [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)
- [A Complementarity-Based Switch-Fuse System for Improved Visual Place Recognition](https://mlsanity.com/paper/2303.00714/) (2023)
- [An Evaluation and Ranking of Different Voting Schemes for Improved Visual Place Recognition](https://mlsanity.com/paper/2305.05705/) (2023)
- [Multi-Technique Sequential Information Consistency For Dynamic Visual Place Recognition In Changing Environments](https://mlsanity.com/paper/2401.08263/) (2024)
- [Employing Universal Voting Schemes for Improved Visual Place Recognition Performance](https://mlsanity.com/paper/2405.02297/) (2024)
- [FRED: A Multi-Modal Autonomous Driving Dataset for Flooded Road Environments](https://mlsanity.com/paper/2605.22018/) (2026)

## Abstract (arXiv)

Visual place recognition (VPR), a fundamental task in computer vision and robotics, is the problem of identifying a place mainly based on visual information. Viewpoint and appearance changes, such as due to weather and seasonal variations, make this task challenging. Currently, there is no universal VPR technique that can work in all types of environments, on a variety of robotic platforms, and under a wide range of viewpoint and appearance changes. Recent work has shown the potential of combining different VPR methods intelligently by evaluating complementarity for some specific VPR datasets to achieve better performance. This, however, requires ground truth information (correct matches) which is not available when a robot is deployed in a real-world scenario. Moreover, running multiple VPR techniques in parallel may be prohibitive for resource-constrained embedded platforms. To overcome these limitations, this paper presents a probabilistic complementarity based switching VPR system, SwitchHit. Our proposed system consists of multiple VPR techniques, however, it does not simply run all techniques at once, rather predicts the probability of correct match for an incoming query image and dynamically switches to another complementary technique if the probability of correctly matching the query is below a certain threshold. This innovative use of multiple VPR techniques allow our system to be more efficient and robust than other combined VPR approaches employing brute force and running multiple VPR techniques at once. Thus making it more suitable for resource constrained embedded systems and achieving an overall superior performance from what any individual VPR method in the system could have by achieved running independently.