# Visual Place Recognition for Aerial Robotics: Exploring Accuracy-Computation Trade-off for Local Image Descriptors

arXiv: https://arxiv.org/abs/1908.00258 · HTML: https://mlsanity.com/paper/1908.00258/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2019.

## Built on by (most cited first, 10 of 10)

- [Improving Visual Place Recognition Performance by Maximising Complementarity](https://mlsanity.com/paper/2102.08416/) (2021)
- [An Efficient and Scalable Collection of Fly-inspired Voting Units for Visual Place Recognition in Changing Environments](https://mlsanity.com/paper/2109.10986/) (2021)
- [Highly-Efficient Binary Neural Networks for Visual Place Recognition](https://mlsanity.com/paper/2202.12375/) (2022)
- [A-MuSIC: An Adaptive Ensemble System For Visual Place Recognition In Changing Environments](https://mlsanity.com/paper/2303.14247/) (2023)
- [Visual Place Recognition with Low-Resolution Images](https://mlsanity.com/paper/2305.05776/) (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)
- [Merging Classification Predictions with Sequential Information for Lightweight Visual Place Recognition in Changing Environments](https://mlsanity.com/paper/2210.00834/) (2022)
- [Patch-DrosoNet: Classifying Image Partitions With Fly-Inspired Models For Lightweight Visual Place Recognition](https://mlsanity.com/paper/2305.05256/) (2023)
- [Aggregating Multiple Bio-Inspired Image Region Classifiers For Effective And Lightweight Visual Place Recognition](https://mlsanity.com/paper/2312.12995/) (2023)

## Abstract (arXiv)

Visual Place Recognition (VPR) is a fundamental yet challenging task for small Unmanned Aerial Vehicle (UAV). The core reasons are the extreme viewpoint changes, and limited computational power onboard a UAV which restricts the applicability of robust but computation intensive state-of-the-art VPR methods. In this context, a viable approach is to use local image descriptors for performing VPR as these can be computed relatively efficiently without the need of any special hardware, such as a GPU. However, the choice of a local feature descriptor is not trivial and calls for a detailed investigation as there is a trade-off between VPR accuracy and the required computational effort. To fill this research gap, this paper examines the performance of several state-of-the-art local feature descriptors, both from accuracy and computational perspectives, specifically for VPR application utilizing standard aerial datasets. The presented results confirm that a trade-off between accuracy and computational effort is inevitable while executing VPR on resource-constrained hardware.