论文标题
VPAIR-在大型户外环境中的空中视觉位置识别和本地化
VPAIR -- Aerial Visual Place Recognition and Localization in Large-scale Outdoor Environments
论文作者
论文摘要
视觉位置识别和视觉定位是自动驾驶汽车的导航和映射的重要组成部分，尤其是在受GNSS贬低的导航方案中。最近的工作集中在地面或接近地面应用上，例如自动驾驶汽车或室内扫描和低空无人机飞行。但是，诸如城市空气流动性之类的应用需要在中高海拔地区的大规模户外环境中进行操作。我们提出了一个名为VPAIR的新数据集。该数据集记录在板上的一架轻型飞机上，在地面上方超过300米的高度上捕获了带有向下的摄像头的图像。每个图像与高分辨率参考渲染配对，包括密集的深度信息和6-DOF参考姿势。该数据集涵盖了超过一百公里的轨迹，超过各种具有挑战性的景观，例如城市，农田和森林。该数据集上的实验说明了视角变化对鸟类视图（例如平面旋转）所带来的挑战。
Visual Place Recognition and Visual Localization are essential components in navigation and mapping for autonomous vehicles especially in GNSS-denied navigation scenarios. Recent work has focused on ground or close to ground applications such as self-driving cars or indoor-scenarios and low-altitude drone flights. However, applications such as Urban Air Mobility require operations in large-scale outdoor environments at medium to high altitudes. We present a new dataset named VPAIR. The dataset was recorded on board a light aircraft flying at an altitude of more than 300 meters above ground capturing images with a downwardfacing camera. Each image is paired with a high resolution reference render including dense depth information and 6-DoF reference poses. The dataset covers a more than one hundred kilometers long trajectory over various types of challenging landscapes, e.g. urban, farmland and forests. Experiments on this dataset illustrate the challenges introduced by the change in perspective to a bird's eye view such as in-plane rotations.
