LF-PGVIO：一种面向大视场相机的使用点与测地线段的视觉惯性里程计框架
LF-PGVIO: A Visual-Inertial-Odometry Framework for Large Field-of-View Cameras using Points and Geodesic Segments
AI总结：
针对大视场甚至带负平面的全向相机点线里程计潜力未充分发挥的问题，提出融合点与测地线段的LF-PGVIO视觉惯性里程计框架，搭配全向曲线段检测方法与适配大畸变图像的相机模型，经公开数据集验证精度和鲁棒性优于现有最优方法，代码将开源。
摘要：
本文提出LF-PGVIO，这是一种面向带负平面的大视场（FoV）相机的视觉惯性里程计（VIO，Visual-Inertial-Odometry）框架，使用点特征与测地线段实现。本研究旨在挖掘大视场全向相机下点线里程计的潜力，甚至适配具备负平面视场的相机。为此，我们提出了全向曲线段检测（OCSD，Omnidirectional Curve Segment Detection）方法，并搭配适用于大畸变图像（如全景环形图像、鱼眼图像及各类全景图像）的相机模型。该方法依据弧度将测地线段切分为多个直线段，提取描述子后再进行重组，通过描述子匹配建立多帧间3D线段的约束关系。在我们的VIO系统中，还对线段特征残差进行了扩展以支持大视场相机。在公开数据集上的大量评估表明，LF-PGVIO的精度与鲁棒性优于现有最优方法。其源代码将在https://github.com/flysoaryun/LF-PGVIO公开发布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
