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.

翻译：本文提出LF-PGVIO，一种针对含负平面的大视场（FoV）相机的点-线视觉-惯导里程计（VIO）框架。本研究旨在释放大视场全向相机（包括具备负平面视场的相机）在点-线里程计中的潜力。为此，我们提出一种结合相机模型的全向曲线段检测（OCSD）方法，该方法适用于大畸变图像（如全景环带图像、鱼眼图像及各类全景图像）。测地线段基于弧度被切分为多个直线段，并对其提取与重组描述子。描述子匹配建立了多帧中三维线段的约束关系。在VIO系统中，线特征残差亦被扩展以支持大视场相机。基于公开数据集的广泛评估表明，与现有最先进方法相比，LF-PGVIO具有更优的精度与鲁棒性。源代码将发布于https://github.com/flysoaryun/LF-PGVIO。