HD Maps: Fine-Grained Road Segmentation by Parsing Ground and Aerial Images
HD Maps: Fine-Grained Road Segmentation by Parsing Ground and Aerial Images
复制标题
DOI：
10.1109/cvpr.2016.393
复制
发表时间：
2016-06
期刊：
影响因子：
--
通讯作者：
G. Máttyus;Shenlong Wang;S. Fidler;R. Urtasun
中科院分区：
文献类型：
--
作者：
G. Máttyus;Shenlong Wang;S. Fidler;R. Urtasun
在本文中，我们提出了一种方法来增强现有的地图与细粒度的分割类别，如停车位和人行道，以及道路车道的数量和位置。为了实现这一目标，我们提出了一种有效的方法，能够估计这些细粒度的类别进行联合推理，单目航拍图像，以及地面图像从立体相机对安装在汽车顶部。重要的是这两种类型的图像之间的对齐的推理，因为即使使用复杂的GPS+IMU系统进行测量，这种对齐也不够准确。我们证明了我们的方法在一个新的数据集上的有效性，该数据集增强了KITTI [8]，使用安装在飞机上的相机拍摄的航拍图像，并在德国的卡尔斯鲁厄市周围飞行。
In this paper we present an approach to enhance existing maps with fine grained segmentation categories such as parking spots and sidewalk, as well as the number and location of road lanes. Towards this goal, we propose an efficient approach that is able to estimate these fine grained categories by doing joint inference over both, monocular aerial imagery, as well as ground images taken from a stereo camera pair mounted on top of a car. Important to this is reasoning about the alignment between the two types of imagery, as even when the measurements are taken with sophisticated GPS+IMU systems, this alignment is not sufficiently accurate. We demonstrate the effectiveness of our approach on a new dataset which enhances KITTI [8] with aerial images taken with a camera mounted on an airplane and flying around the city of Karlsruhe, Germany.