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Simultaneous Localization And Mapping (SLAM) has become a crucial aspect inthe fields of autonomous driving and robotics. One crucial component of visualSLAM is the Field-of-View (FoV) of the camera, as a larger FoV allows for awider range of surrounding elements and features to be perceived. However, whenthe FoV of the camera reaches the negative half-plane, traditional methods forrepresenting image feature points using [u,v,1]^T become ineffective. While thepanoramic FoV is advantageous for loop closure, its benefits are not easilyrealized under large-attitude-angle differences where loop-closure framescannot be easily matched by existing methods. As loop closure on wide-FoVpanoramic data further comes with a large number of outliers, traditionaloutlier rejection methods are not directly applicable. To address these issues,we propose LF-VISLAM, a Visual Inertial SLAM framework for cameras withextremely Large FoV with loop closure. A three-dimensional vector with unitlength is introduced to effectively represent feature points even on thenegative half-plane. The attitude information of the SLAM system is leveragedto guide the feature point detection of the loop closure. Additionally, a newoutlier rejection method based on the unit length representation is integratedinto the loop closure module. We collect the PALVIO dataset using a PanoramicAnnular 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) toaddress the lack of panoramic SLAM datasets. Experiments on the establishedPALVIO and public datasets show that the proposed LF-VISLAM outperformsstate-of-the-art SLAM methods. Our code will be open-sourced athttps://github.com/flysoaryun/LF-VISLAM.

LF-VISLAM: A SLAM Framework for Large Field-of-View Cameras with Negative Imaging Plane on Mobile Agents