  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.
