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-SLAM, a 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 IMU sensor to address the lack of panoramic SLAM datasets. Experiments on the established PALVIO and public datasets show that the proposed LF-SLAM outperforms state-of-the-art SLAM methods. Our code will be open-sourced at https://github.com/flysoaryun/LF-SLAM.

翻译：同时定位与地图构建已成为自动驾驶和机器人领域的关键技术。视觉SLAM的核心组成部分之一是相机的视场角——更大的视场角可感知更广泛的周围环境和特征。然而，当相机视场角达到负半平面时，传统使用[u,v,1]^T表示图像特征点的方法将失效。虽然全景视场角有利于闭环检测，但在大姿态角差异条件下其优势难以发挥，现有方法无法有效匹配闭环帧。此外，宽视场全景数据的闭环检测会伴随大量异常值，传统异常值剔除方法无法直接应用。针对上述问题，我们提出LF-SLAM——面向超广角相机的带有闭环功能的SLAM框架。采用单位长度三维向量有效表征负半平面特征点，利用SLAM系统的姿态信息引导闭环检测的特征点提取，并集成基于单位长度表征的新型异常值剔除方法于闭环模块。为弥补全景SLAM数据集的不足，我们采用全景环形透镜系统（覆盖360°×(40°-120°)全视场角）配合IMU传感器采集了PALVIO数据集。在自建PALVIO数据集和公开数据集上的实验表明，所提出的LF-SLAM方法优于现有最先进的SLAM方案。我们的代码将开源在https://github.com/flysoaryun/LF-SLAM。