# Design and Identification of Keypoint Patches in Unstructured Environments

arXiv: https://arxiv.org/abs/2410.00521 · HTML: https://mlsanity.com/paper/2410.00521/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 1 Oct 2024.

## Builds on (most cited first, 8 of 8)

- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [LoFTR: Detector-Free Local Feature Matching with Transformers](https://mlsanity.com/paper/2104.00680/) (2021)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [Image Matching Using SIFT, SURF, BRIEF and ORB: Performance Comparison for Distorted Images](https://mlsanity.com/paper/1710.02726/) (2017)
- [Deep ChArUco: Dark ChArUco Marker Pose Estimation](https://mlsanity.com/paper/1812.03247/) (2018)
- [DeepTag: A General Framework for Fiducial Marker Design and Detection](https://mlsanity.com/paper/2105.13731/) (2021)
- [TopoTag: A Robust and Scalable Topological Fiducial Marker System](https://mlsanity.com/paper/1908.01450/) (2019)

## Abstract (arXiv)

Reliable perception of targets is crucial for the stable operation of autonomous robots. A widely preferred method is keypoint identification in an image, as it allows direct mapping from raw images to 2D coordinates, facilitating integration with other algorithms like localization and path planning. In this study, we closely examine the design and identification of keypoint patches in cluttered environments, where factors such as blur and shadows can hinder detection. We propose four simple yet distinct designs that consider various scale, rotation and camera projection using a limited number of pixels. Additionally, we customize the Superpoint network to ensure robust detection under various types of image degradation. The effectiveness of our approach is demonstrated through real-world video tests, highlighting potential for vision-based autonomous systems.