LF-VISLAM：面向移动智能体上具有负成像平面的大视场相机的SLAM框架
LF-VISLAM: A SLAM Framework for Large Field-of-View Cameras with Negative Imaging Plane on Mobile Agents
AI总结：
提出LF-VISLAM框架，通过单位长度三维向量表示特征点并利用姿态信息引导回环检测，解决大视场相机负半平面成像和异常值剔除问题，在PALVIO及公开数据集上超越现有SLAM方法。
摘要：
同步定位与建图（SLAM）已成为自动驾驶和机器人领域的关键技术。视觉SLAM的一个重要组成部分是相机的视场（FoV），因为更大的视场能够感知更广泛的周围环境和特征。然而，当相机的视场达到负半平面时，使用[u,v,1]^T表示图像特征点的传统方法将失效。虽然全景视场有利于回环检测，但在大姿态角差异下，其优势不易实现，因为现有的方法难以匹配回环帧。由于宽视场全景数据上的回环检测还伴随大量外点，传统的异常值剔除方法不直接适用。为解决这些问题，我们提出了LF-VISLAM，一个面向具有极大视场且带回环检测的相机的视觉惯性SLAM框架。引入了一个单位长度的三维向量，即使在负半平面上也能有效表示特征点。利用SLAM系统的姿态信息来引导回环检测的特征点提取。此外，在回环检测模块中集成了一种基于单位长度表示的新的异常值剔除方法。我们使用全景环形透镜（PAL）系统（总视场为360°x(40°~120°)）和惯性测量单元（IMU）收集了PALVIO数据集，用于视觉惯性里程计（VIO），以解决全景SLAM数据集缺乏的问题。在建立的PALVIO和公开数据集上的实验表明，所提出的LF-VISLAM优于最先进的SLAM方法。我们的代码将在https://github.com/flysoaryun/LF-VISLAM开源。
英文摘要：
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°x(40°~120°) 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
