# LF-PGVIO: A Visual-Inertial-Odometry Framework for Large Field-of-View Cameras using Points and Geodesic Segments

arXiv: https://arxiv.org/abs/2306.06663 · HTML: https://mlsanity.com/paper/2306.06663/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 59 references that are in the map.
- First posted on arXiv 11 Jun 2023.

## Builds on (most cited first, 18 of 18)

- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [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)
- [OpenVSLAM: A Versatile Visual SLAM Framework](https://mlsanity.com/paper/1910.01122/) (2019)
- [Holistically-Attracted Wireframe Parsing](https://mlsanity.com/paper/2003.01663/) (2020)
- [DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients](https://mlsanity.com/paper/2212.07766/) (2022)
- [PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segments](https://mlsanity.com/paper/1705.09479/) (2017)
- [SOLD2: Self-supervised Occlusion-aware Line Description and Detection](https://mlsanity.com/paper/2104.03362/) (2021)
- [Review on Panoramic Imaging and Its Applications in Scene Understanding](https://mlsanity.com/paper/2205.05570/) (2022)
- [ELSED: Enhanced Line SEgment Drawing](https://mlsanity.com/paper/2108.03144/) (2021)
- [UV-SLAM: Unconstrained Line-based SLAM Using Vanishing Points for Structural Mapping](https://mlsanity.com/paper/2112.13515/) (2021)
- [PL-VINS: Real-Time Monocular Visual-Inertial SLAM with Point and Line Features](https://mlsanity.com/paper/2009.07462/) (2020)
- [StructVIO : Visual-inertial Odometry with Structural Regularity of Man-made Environments](https://mlsanity.com/paper/1810.06796/) (2018)
- [ULSD: Unified Line Segment Detection across Pinhole, Fisheye, and Spherical Cameras](https://mlsanity.com/paper/2011.03174/) (2020)
- [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)
- [Annular Computational Imaging: Capture Clear Panoramic Images through Simple Lens](https://mlsanity.com/paper/2206.06070/) (2022)

## Built on by (most cited first, 3 of 3)

- [QuaDreamer: Controllable Panoramic Video Generation for Quadruped Robots](https://mlsanity.com/paper/2508.02512/) (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)

## Abstract (arXiv)

In this paper, we propose LF-PGVIO, a Visual-Inertial-Odometry (VIO) framework for large Field-of-View (FoV) cameras with a negative plane using points and geodesic segments. The purpose of our research is to unleash the potential of point-line odometry with large-FoV omnidirectional cameras, even for cameras with negative-plane FoV. To achieve this, we propose an Omnidirectional Curve Segment Detection (OCSD) method combined with a camera model which is applicable to images with large distortions, such as panoramic annular images, fisheye images, and various panoramic images. The geodesic segment is sliced into multiple straight-line segments based on the radian and descriptors are extracted and recombined. Descriptor matching establishes the constraint relationship between 3D line segments in multiple frames. In our VIO system, line feature residual is also extended to support large-FoV cameras. Extensive evaluations on public datasets demonstrate the superior accuracy and robustness of LF-PGVIO compared to state-of-the-art methods. The source code will be made publicly available at https://github.com/flysoaryun/LF-PGVIO.