# Are State-of-the-art Visual Place Recognition Techniques any Good for Aerial Robotics?

arXiv: https://arxiv.org/abs/1904.07967 · HTML: https://mlsanity.com/paper/1904.07967/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2019.

## Built on by (most cited first, 9 of 9)

- [Where is your place, Visual Place Recognition?](https://mlsanity.com/paper/2103.06443/) (2021)
- [VPAIR -- Aerial Visual Place Recognition and Localization in Large-scale Outdoor Environments](https://mlsanity.com/paper/2205.11567/) (2022)
- [Sequence-Based Filtering for Visual Route-Based Navigation: Analysing the Benefits, Trade-offs and Design Choices](https://mlsanity.com/paper/2103.01994/) (2021)
- [Visual Place Recognition with Low-Resolution Images](https://mlsanity.com/paper/2305.05776/) (2023)
- [Scene Retrieval for Contextual Visual Mapping](https://mlsanity.com/paper/2102.12728/) (2021)
- [A Benchmark Comparison of Visual Place Recognition Techniques for Resource-Constrained Embedded Platforms](https://mlsanity.com/paper/2109.11002/) (2021)
- [Aggregating Multiple Bio-Inspired Image Region Classifiers For Effective And Lightweight Visual Place Recognition](https://mlsanity.com/paper/2312.12995/) (2023)
- [Visual Place Recognition for Large-Scale UAV Applications](https://mlsanity.com/paper/2507.15089/) (2025)
- [M3GA-Wild: A Large-Scale Dataset and Benchmark for Multi-Modal Multi-session Ground-to-Aerial Place Recognition in Forests](https://mlsanity.com/paper/2609.23003/) (2026)

## Abstract (arXiv)

Visual Place Recognition (VPR) has seen significant advances at the frontiers of matching performance and computational superiority over the past few years. However, these evaluations are performed for ground-based mobile platforms and cannot be generalized to aerial platforms. The degree of viewpoint variation experienced by aerial robots is complex, with their processing power and on-board memory limited by payload size and battery ratings. Therefore, in this paper, we collect 8 state-of-the-art VPR techniques that have been previously evaluated for ground-based platforms and compare them on 2 recently proposed aerial place recognition datasets with three prime focuses: a) Matching performance b) Processing power consumption c) Projected memory requirements. This gives a birds-eye view of the applicability of contemporary VPR research to aerial robotics and lays down the the nature of challenges for aerial-VPR.