# 360ORB-SLAM: A Visual SLAM System for Panoramic Images with Depth Completion Network

arXiv: https://arxiv.org/abs/2401.10560 · HTML: https://mlsanity.com/paper/2401.10560/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 19 Jan 2024.

## Builds on (most cited first, 7 of 7)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [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)
- [OpenVSLAM: A Versatile Visual SLAM Framework](https://mlsanity.com/paper/1910.01122/) (2019)
- [Confidence Propagation through CNNs for Guided Sparse Depth Regression](https://mlsanity.com/paper/1811.01791/) (2018)
- [Omnidirectional DSO: Direct Sparse Odometry with Fisheye Cameras](https://mlsanity.com/paper/1808.02775/) (2018)

## Built on by (most cited first, 5 of 5)

- [Depth Anywhere: Enhancing 360 Monocular Depth Estimation via Perspective Distillation and Unlabeled Data Augmentation](https://mlsanity.com/paper/2406.12849/) (2024)
- [Panoramic Affordance Prediction](https://mlsanity.com/paper/2603.15558/) (2026)
- [PanoImager: Geometry-Guided Novel View Synthesis and Reconstruction from Sparse Panoramic Views](https://mlsanity.com/paper/2606.27071/) (2026)
- [Incremental Mapping with Measurement Synchronization & Compression](https://mlsanity.com/paper/2602.07901/) (2026)
- [Cube-Splat: High-Fidelity 360deg Gaussian Splatting SLAM via Cubemap Factorization and Adjoint-Consistent Optimization](https://mlsanity.com/paper/2609.21347/) (2026)

## Abstract (arXiv)

To enhance the performance and effect of AR/VR applications and visual assistance and inspection systems, visual simultaneous localization and mapping (vSLAM) is a fundamental task in computer vision and robotics. However, traditional vSLAM systems are limited by the camera's narrow field-of-view, resulting in challenges such as sparse feature distribution and lack of dense depth information. To overcome these limitations, this paper proposes a 360ORB-SLAM system for panoramic images that combines with a depth completion network. The system extracts feature points from the panoramic image, utilizes a panoramic triangulation module to generate sparse depth information, and employs a depth completion network to obtain a dense panoramic depth map. Experimental results on our novel panoramic dataset constructed based on Carla demonstrate that the proposed method achieves superior scale accuracy compared to existing monocular SLAM methods and effectively addresses the challenges of feature association and scale ambiguity. The integration of the depth completion network enhances system stability and mitigates the impact of dynamic elements on SLAM performance.