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.

翻译：同步定位与地图构建（SLAM）已成为自动驾驶和机器人领域的关键技术。视觉SLAM的核心要素之一是相机的视场角（FoV），更大的视场角可感知更广范围的周围环境和特征。然而，当相机视场角延伸至负半平面时，传统采用[u,v,1]^T表征图像特征点的方法将失效。尽管全景视场角有利于闭环检测，但在大姿态角差异场景下，现有方法难以有效匹配闭环帧，使得全景视场角的优势难以发挥。此外，宽视场角全景数据的闭环检测伴随大量外点，传统外点剔除方法无法直接适用。为解决上述问题，我们提出LF-VISLAM——一个面向具备闭环检测的超大视场角相机的视觉惯性SLAM框架。通过引入单位长度的三维向量，即使在负半平面也能有效表征特征点；利用SLAM系统的姿态信息指导闭环检测的特征点提取；同时将基于单位长度表征的新型外点剔除方法集成至闭环检测模块。我们采用全视场角为360°×（40°~120°）的全景环形透镜（PAL）系统和惯性测量单元（IMU）构建用于视觉惯性里程计（VIO）的PALVIO数据集，以填补全景SLAM数据集的空白。在自主采集的PALVIO数据集及公开数据集上的实验表明，所提出的LF-VISLAM方法优于当前最先进的SLAM方法。相关代码将在https://github.com/flysoaryun/LF-VISLAM开源。