Robust Ego-motion Estimation and Map Matching Technique for Autonomous Vehicle Localization with High Definition Digital Map
Robust Ego-motion Estimation and Map Matching Technique for Autonomous Vehicle Localization with High Definition Digital Map
复制标题
高精度数字地图自主车辆定位的鲁棒自运动估计和地图匹配技术
DOI：
10.1109/ictc.2018.8539518
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Jeongdan Choi
中科院分区：
文献类型：
--
作者：
Seung;Jungyu Kang;Yongwoo Jo;Dongjin Lee;Jeongdan Choi
自动驾驶车辆的环境识别所需的基本技术之一是识别车辆位置和方向的定位技术。与先前的从传感器数据本身生成地图数据的定位技术相比，使用高清晰度（HD）数字地图的研究越来越多。基于地图的定位技术是通过车辆自身的运动预测下一步的位置，并通过地图匹配确定位置。在本文中，我们提出了一个强大的自我运动估计和地图匹配技术的鲁棒车辆定位。首先，我们提出了一个强大的自我运动估计的视觉里程计（VO）模型和车辆平面运动模型的基础上，在车辆传感器，以提高在图像特征的情况下，VO的鲁棒性。我们还介绍了一个新的线分割匹配模型和几何校正方法提取的道路标记从一个反透视映射（IPM）的鲁棒地图匹配技术。本文提出的技术已经通过真实的自动驾驶汽车进行了多种方式的验证，并成功获得了大韩民国的自动驾驶执照。
One of the essential technologies required for environmental recognition of an autonomous vehicle is a localization technique that recognizes the position and orientation of the vehicle. In contrast to previous localization techniques that generate map data from sensor data itself, there is an increasing number of studies using high definition (HD) digital maps. The map-based localization technology consists of predicting the position of the next step through the ego-motion of the vehicle and determining the position through map matching. In this paper, we propose a robust ego-motion estimation and map matching technology for robust vehicle localization. First, we propose a visual odometry (VO) model for robust ego-motion estimation and a vehicle planar motion model based on in-vehicle sensors to improve the robustness of VO in the absence of image features. We also introduce a new line segmentation matching model and a geometric correction method of extracted road marking from an inverse perspective mapping (IPM) for robust map matching techniques. The technology proposed in this paper has been verified in various ways through real autonomous vehicles and successfully acquired the autonomous driving license of the Republic of Korea.