论文标题
LF-VIO：一个带负平面的大型视野摄像机的视觉惯性化学框架
LF-VIO: A Visual-Inertial-Odometry Framework for Large Field-of-View Cameras with Negative Plane
论文作者
论文摘要
视觉惯性 - 调节法吸引了自主驾驶和机器人技术领域的广泛关注。视场（FOV）的大小在视觉播音（VO）和视觉惯性 - 射频法（VO）中起着重要作用，因为大型FOV可以感知各种周围的场景元素和特征。但是，当相机的字段到达负半平面时，就不能简单地使用[u，v，1]^t来表示图像特征点。为了解决这个问题，我们建议LF-VIO，这是一个非常大的FOV的相机的实时VIO框架。我们利用具有单位长度的三维矢量来表示特征点，并设计一系列算法来克服这一挑战。为了解决带有地面位置和姿势的全景视觉探针数据集的稀缺性，我们介绍了Palvio数据集，该数据集用全景环形镜头（PAL）系统收集，整个FOV为360°x（40°-120°）和IMU传感器。有了全面的实验，在已建立的Palvio基准和公共Fisheye摄像机数据集上验证了所提出的LF-VIO，该数据集的FOV为360°X（0°-93.5°）。 LF-VIO优于最先进的视觉惯性 - 调节法。我们的数据集和代码可在https://github.com/flysoaryun/lf-vio上公开提供。
Visual-inertial-odometry has attracted extensive attention in the field of autonomous driving and robotics. The size of Field of View (FoV) plays an important role in Visual-Odometry (VO) and Visual-Inertial-Odometry (VIO), as a large FoV enables to perceive a wide range of surrounding scene elements and features. However, when the field of the camera reaches the negative half plane, one cannot simply use [u,v,1]^T to represent the image feature points anymore. To tackle this issue, we propose LF-VIO, a real-time VIO framework for cameras with extremely large FoV. We leverage a three-dimensional vector with unit length to represent feature points, and design a series of algorithms to overcome this challenge. To address the scarcity of panoramic visual odometry datasets with ground-truth location and pose, we present the PALVIO dataset, collected with a Panoramic Annular Lens (PAL) system with an entire FoV of 360°x(40°-120°) and an IMU sensor. With a comprehensive variety of experiments, the proposed LF-VIO is verified on both the established PALVIO benchmark and a public fisheye camera dataset with a FoV of 360°x(0°-93.5°). LF-VIO outperforms state-of-the-art visual-inertial-odometry methods. Our dataset and code are made publicly available at https://github.com/flysoaryun/LF-VIO
