# Investigating Outdoor Recognition Performance of Infrared Beacons for Infrastructure-based Localization

arXiv: https://arxiv.org/abs/2104.09335 · HTML: https://mlsanity.com/paper/2104.09335/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 19 Apr 2021.

## Builds on (most cited first, 4 of 4)

- [Visual Camera Re-Localization from RGB and RGB-D Images Using DSAC](https://mlsanity.com/paper/2002.12324/) (2020)
- [Semantic Visual Localization](https://mlsanity.com/paper/1712.05773/) (2017)
- [Monocular Vehicle Self-localization method based on Compact Semantic Map](https://mlsanity.com/paper/1805.06155/) (2018)
- [CoBe -- Coded Beacons for Localization, Object Tracking, and SLAM Augmentation](https://mlsanity.com/paper/1708.05625/) (2017)

## Built on by (most cited first, 1 of 1)

- [InfraParis: A multi-modal and multi-task autonomous driving dataset](https://mlsanity.com/paper/2309.15751/) (2023)

## Abstract (arXiv)

This paper demonstrates a system comprised of infrared beacons and a camera equipped with an optical band-pass filter. Our system can reliably detect and identify individual beacons at 100m distance regardless of lighting conditions. We describe the camera and beacon design as well as the image processing pipeline in detail. In our experiments, we investigate and demonstrate the ability of the system to recognize our beacons in both daytime and nighttime conditions. High precision localization is a key enabler for automated vehicles but remains unsolved, despite strong recent improvements. Our low-cost, infrastructure-based approach is a potential step towards solving the localization problem. All datasets are made available here https://embedded.rwth-aachen.de/doku.php?id=forschung:mobility:infralocalization:concept.