LF-VIO：負平面を含む超広視野カメラ向け視覚慣性オドメトリフレームワーク
LF-VIO: A Visual-Inertial-Odometry Framework for Large Field-of-View Cameras with Negative Plane
視野が負の半平面に達する超広角カメラでも使えるリアルタイム視覚慣性オドメトリを、特徴点を単位3次元ベクトルで表現する手法で実現し、全天球型データセットPALVIOも公開した論文。
著者: Ze Wang, Kailun Yang, Hao Shi, Peng Li, Fei Gao, Kaiwei Wang
分類: cs.CV, cs.RO, eess.IV
原文アブストラクト
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{\deg}x(40{\deg}-120{\deg}) 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{\deg}x(0{\deg}-93.5{\deg}). 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
