# Multi-Technique Sequential Information Consistency For Dynamic Visual Place Recognition In Changing Environments

arXiv: https://arxiv.org/abs/2401.08263 · HTML: https://mlsanity.com/paper/2401.08263/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 39 references that are in the map.
- First posted on arXiv 16 Jan 2024.

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

- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [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)
- [Where is your place, Visual Place Recognition?](https://mlsanity.com/paper/2103.06443/) (2021)
- [On the Performance of ConvNet Features for Place Recognition](https://mlsanity.com/paper/1501.04158/) (2015)
- [Multi-Process Fusion: Visual Place Recognition Using Multiple Image Processing Methods](https://mlsanity.com/paper/1903.03305/) (2019)
- [Lightweight Unsupervised Deep Loop Closure](https://mlsanity.com/paper/1805.07703/) (2018)
- [Hyperdimensional computing as a framework for systematic aggregation of image descriptors](https://mlsanity.com/paper/2101.07720/) (2021)
- [Visual Place Recognition: A Tutorial](https://mlsanity.com/paper/2303.03281/) (2023)
- [Hierarchical Multi-Process Fusion for Visual Place Recognition](https://mlsanity.com/paper/2002.03895/) (2020)
- [Improving Visual Place Recognition Performance by Maximising Complementarity](https://mlsanity.com/paper/2102.08416/) (2021)
- [Convolutional Neural Network-Based Image Representation for Visual Loop Closure Detection](https://mlsanity.com/paper/1504.05241/) (2015)
- [SwitchHit: A Probabilistic, Complementarity-Based Switching System for Improved Visual Place Recognition in Changing Environments](https://mlsanity.com/paper/2203.00591/) (2022)

## Abstract (arXiv)

Visual place recognition (VPR) is an essential component of robot navigation and localization systems that allows them to identify a place using only image data. VPR is challenging due to the significant changes in a place's appearance driven by different daily illumination, seasonal weather variations and diverse viewpoints. Currently, no single VPR technique excels in every environmental condition, each exhibiting unique benefits and shortcomings, and therefore combining multiple techniques can achieve more reliable VPR performance. Present multi-method approaches either rely on online ground-truth information, which is often not available, or on brute-force technique combination, potentially lowering performance with high variance technique sets. Addressing these shortcomings, we propose a VPR system dubbed Multi-Sequential Information Consistency (MuSIC) which leverages sequential information to select the most cohesive technique on an online per-frame basis. For each technique in a set, MuSIC computes their respective sequential consistencies by analysing the frame-to-frame continuity of their top match candidates, which are then directly compared to select the optimal technique for the current query image. The use of sequential information to select between VPR methods results in an overall VPR performance increase across different benchmark datasets, while avoiding the need for extra ground-truth of the runtime environment.