  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 360{\deg}x(40{\deg}~120{\deg})
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.
