# LF-VISLAM: A SLAM Framework for Large Field-of-View Cameras with Negative Imaging Plane on Mobile Agents

arXiv: https://arxiv.org/abs/2209.05167 · HTML: https://mlsanity.com/paper/2209.05167/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 66 references that are in the map.
- First posted on arXiv 12 Sep 2022.

## Builds on (most cited first, 29 of 29)

- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [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)
- [FAST-LIO2: Fast Direct LiDAR-inertial Odometry](https://mlsanity.com/paper/2107.06829/) (2021)
- [The TUM VI Benchmark for Evaluating Visual-Inertial Odometry](https://mlsanity.com/paper/1804.06120/) (2018)
- [On-Manifold Preintegration for Real-Time Visual-Inertial Odometry](https://mlsanity.com/paper/1512.02363/) (2015)
- [Scan Context++: Structural Place Recognition Robust to Rotation and Lateral Variations in Urban Environments](https://mlsanity.com/paper/2109.13494/) (2021)
- [OpenVSLAM: A Versatile Visual SLAM Framework](https://mlsanity.com/paper/1910.01122/) (2019)
- [Capturing Omni-Range Context for Omnidirectional Segmentation](https://mlsanity.com/paper/2103.05687/) (2021)
- [R3LIVE: A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package](https://mlsanity.com/paper/2109.07982/) (2021)
- [Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras](https://mlsanity.com/paper/1708.07878/) (2017)
- [LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping](https://mlsanity.com/paper/2104.10831/) (2021)
- [Review on Panoramic Imaging and Its Applications in Scene Understanding](https://mlsanity.com/paper/2205.05570/) (2022)
- [DS-PASS: Detail-Sensitive Panoramic Annular Semantic Segmentation through SwaftNet for Surrounding Sensing](https://mlsanity.com/paper/1909.07721/) (2019)
- [PanoFlow: Learning 360deg Optical Flow for Surrounding Temporal Understanding](https://mlsanity.com/paper/2202.13388/) (2022)
- [Panoramic Panoptic Segmentation: Towards Complete Surrounding Understanding via Unsupervised Contrastive Learning](https://mlsanity.com/paper/2103.00868/) (2021)
- [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)
- [Omni-swarm: A Decentralized Omnidirectional Visual-Inertial-UWB State Estimation System for Aerial Swarms](https://mlsanity.com/paper/2103.04131/) (2021)
- [LF-VIO: A Visual-Inertial-Odometry Framework for Large Field-of-View Cameras with Negative Plane](https://mlsanity.com/paper/2202.12613/) (2022)
- [ROVO: Robust Omnidirectional Visual Odometry for Wide-baseline Wide-FOV Camera Systems](https://mlsanity.com/paper/1902.11154/) (2019)
- [Panoramic annular SLAM with loop closure and global optimization](https://mlsanity.com/paper/2102.13400/) (2021)
- [SalientDSO: Bringing Attention to Direct Sparse Odometry](https://mlsanity.com/paper/1803.00127/) (2018)
- [A Multimodal Vision Sensor for Autonomous Driving](https://mlsanity.com/paper/1908.05649/) (2019)
- [Accurate Object Association and Pose Updating for Semantic SLAM](https://mlsanity.com/paper/2012.11368/) (2020)
- [Improved Real-Time Monocular SLAM Using Semantic Segmentation on Selective Frames](https://mlsanity.com/paper/2105.00114/) (2021)
- [MuCaSLAM: CNN-Based Frame Quality Assessment for Mobile Robot with Omnidirectional Visual SLAM](https://mlsanity.com/paper/2209.01936/) (2022)

## Built on by (most cited first, 11 of 11)

- [A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision](https://mlsanity.com/paper/2502.10444/) (2025)
- [MG-SLAM: Structure Gaussian Splatting SLAM with Manhattan World Hypothesis](https://mlsanity.com/paper/2405.20031/) (2024)
- [OmniGS: Fast Radiance Field Reconstruction using Omnidirectional Gaussian Splatting](https://mlsanity.com/paper/2404.03202/) (2024)
- [QuaDreamer: Controllable Panoramic Video Generation for Quadruped Robots](https://mlsanity.com/paper/2508.02512/) (2025)
- [LF-PGVIO: A Visual-Inertial-Odometry Framework for Large Field-of-View Cameras using Points and Geodesic Segments](https://mlsanity.com/paper/2306.06663/) (2023)
- [UniMPR: A Unified Framework for Multimodal Place Recognition with Heterogeneous Sensor Configurations](https://mlsanity.com/paper/2512.18279/) (2025)
- [P2U-SLAM: A Monocular Wide-FoV SLAM System Based on Point Uncertainty and Pose Uncertainty](https://mlsanity.com/paper/2409.10143/) (2024)
- [EI-Nexus: Towards Unmediated and Flexible Inter-Modality Local Feature Extraction and Matching for Event-Image Data](https://mlsanity.com/paper/2410.21743/) (2024)
- [Look Up and Look Back: Hidden Attention and Latent Orientation in a Frozen Foundation Model for Panoramic SLAM](https://mlsanity.com/paper/2608.00925/) (2026)
- [PanoGS-SLAM: Panoramic 3D Gaussian Splatting SLAM](https://mlsanity.com/paper/2609.17387/) (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)

Simultaneous Localization And Mapping (SLAM) has become a crucial aspect in the fields of autonomous driving and robotics. One crucial component of visual SLAM is the Field-of-View (FoV) of the camera, as a larger FoV allows for a wider range of surrounding elements and features to be perceived. However, when the FoV of the camera reaches the negative half-plane, traditional methods for representing image feature points using [u,v,1]T become ineffective. While the panoramic FoV is advantageous for loop closure, its benefits are not easily realized under large-attitude-angle differences where loop-closure frames cannot be easily matched by existing methods. As loop closure on wide-FoV panoramic data further comes with a large number of outliers, traditional outlier rejection methods are not directly applicable. To address these issues, we propose LF-VISLAM, a Visual Inertial SLAM framework for cameras with extremely Large FoV with loop closure. A three-dimensional vector with unit length is introduced to effectively represent feature points even on the negative half-plane. The attitude information of the SLAM system is leveraged to guide the feature point detection of the loop closure. Additionally, a new outlier rejection method based on the unit length representation is integrated into the loop closure module. We collect the PALVIO dataset using a Panoramic Annular Lens (PAL) system with an entire FoV of 360degx(40deg~120deg) and an Inertial Measurement Unit (IMU) for Visual Inertial Odometry (VIO) to address the lack of panoramic SLAM datasets. Experiments on the established PALVIO and public datasets show that the proposed LF-VISLAM outperforms state-of-the-art SLAM methods. Our code will be open-sourced at https://github.com/flysoaryun/LF-VISLAM.