# Panoramic annular SLAM with loop closure and global optimization

arXiv: https://arxiv.org/abs/2102.13400 · HTML: https://mlsanity.com/paper/2102.13400/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 44 references that are in the map.
- First posted on arXiv 26 Feb 2021.

## Builds on (most cited first, 16 of 16)

- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments](https://mlsanity.com/paper/1809.08379/) (2018)
- [LDSO: Direct Sparse Odometry with Loop Closure](https://mlsanity.com/paper/1808.01111/) (2018)
- [A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors](https://mlsanity.com/paper/1901.03642/) (2019)
- [A Photometrically Calibrated Benchmark For Monocular Visual Odometry](https://mlsanity.com/paper/1607.02555/) (2016)
- [DS-PASS: Detail-Sensitive Panoramic Annular Semantic Segmentation through SwaftNet for Surrounding Sensing](https://mlsanity.com/paper/1909.07721/) (2019)
- [OmniSLAM: Omnidirectional Localization and Dense Mapping for Wide-baseline Multi-camera Systems](https://mlsanity.com/paper/2003.08056/) (2020)
- [Omnidirectional DSO: Direct Sparse Odometry with Fisheye Cameras](https://mlsanity.com/paper/1808.02775/) (2018)
- [CubemapSLAM: A Piecewise-Pinhole Monocular Fisheye SLAM System](https://mlsanity.com/paper/1811.12633/) (2018)
- [ROVO: Robust Omnidirectional Visual Odometry for Wide-baseline Wide-FOV Camera Systems](https://mlsanity.com/paper/1902.11154/) (2019)
- [A Multimodal Vision Sensor for Autonomous Driving](https://mlsanity.com/paper/1908.05649/) (2019)
- [Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors](https://mlsanity.com/paper/1905.05425/) (2019)

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

- [MatchFormer: Interleaving Attention in Transformers for Feature Matching](https://mlsanity.com/paper/2203.09645/) (2022)
- [Review on Panoramic Imaging and Its Applications in Scene Understanding](https://mlsanity.com/paper/2205.05570/) (2022)
- [Minimalist and High-Quality Panoramic Imaging with PSF-aware Transformers](https://mlsanity.com/paper/2306.12992/) (2023)
- [LF-VIO: A Visual-Inertial-Odometry Framework for Large Field-of-View Cameras with Negative Plane](https://mlsanity.com/paper/2202.12613/) (2022)
- [LF-VISLAM: A SLAM Framework for Large Field-of-View Cameras with Negative Imaging Plane on Mobile Agents](https://mlsanity.com/paper/2209.05167/) (2022)
- [HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor](https://mlsanity.com/paper/2107.03180/) (2021)
- [Aerial-PASS: Panoramic Annular Scene Segmentation in Drone Videos](https://mlsanity.com/paper/2105.07209/) (2021)
- [Trans4Map: Revisiting Holistic Bird's-Eye-View Mapping from Egocentric Images to Allocentric Semantics with Vision Transformers](https://mlsanity.com/paper/2207.06205/) (2022)
- [P2U-SLAM: A Monocular Wide-FoV SLAM System Based on Point Uncertainty and Pose Uncertainty](https://mlsanity.com/paper/2409.10143/) (2024)
- [A Multi-modal Garden Dataset and Hybrid 3D Dense Reconstruction Framework Based on Panoramic Stereo Images for a Trimming Robot](https://mlsanity.com/paper/2305.06278/) (2023)

## Abstract (arXiv)

In this paper, we propose panoramic annular simultaneous localization and mapping (PA-SLAM), a visual SLAM system based on panoramic annular lens. A hybrid point selection strategy is put forward in the tracking front-end, which ensures repeatability of keypoints and enables loop closure detection based on the bag-of-words approach. Every detected loop candidate is verified geometrically and the Sim(3) relative pose constraint is estimated to perform pose graph optimization and global bundle adjustment in the back-end. A comprehensive set of experiments on real-world datasets demonstrates that the hybrid point selection strategy allows reliable loop closure detection, and the accumulated error and scale drift have been significantly reduced via global optimization, enabling PA-SLAM to reach state-of-the-art accuracy while maintaining high robustness and efficiency.