# Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors

arXiv: https://arxiv.org/abs/1905.05425 · HTML: https://mlsanity.com/paper/1905.05425/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2019.

## Built on by (most cited first, 4 of 4)

- [Review on Panoramic Imaging and Its Applications in Scene Understanding](https://mlsanity.com/paper/2205.05570/) (2022)
- [Panoramic annular SLAM with loop closure and global optimization](https://mlsanity.com/paper/2102.13400/) (2021)
- [PanoVPR: Towards Unified Perspective-to-Equirectangular Visual Place Recognition via Sliding Windows across the Panoramic View](https://mlsanity.com/paper/2303.14095/) (2023)
- [HypeVPR: Exploring Hyperbolic Space for Perspective to Equirectangular Visual Place Recognition](https://mlsanity.com/paper/2506.04764/) (2025)

## Abstract (arXiv)

Visual localization is an attractive problem that estimates the camera localization from database images based on the query image. It is a crucial task for various applications, such as autonomous vehicles, assistive navigation and augmented reality. The challenging issues of the task lie in various appearance variations between query and database images, including illumination variations, dynamic object variations and viewpoint variations. In order to tackle those challenges, Panoramic Annular Localizer into which panoramic annular lens and robust deep image descriptors are incorporated is proposed in this paper. The panoramic annular images captured by the single camera are processed and fed into the NetVLAD network to form the active deep descriptor, and sequential matching is utilized to generate the localization result. The experiments carried on the public datasets and in the field illustrate the validation of the proposed system.