Gakne Paul Verlaine, O'Keefe Kyle
Position, Location and Navigation (PLAN) Group, Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Drive, N.W., Calgary, AB T2N 1N4, Canada.
Sensors (Basel). 2018 Apr 17;18(4):1244. doi: 10.3390/s18041244.
This paper presents a method of fusing the ego-motion of a robot or a land vehicle estimated from an upward-facing camera with Global Navigation Satellite System (GNSS) signals for navigation purposes in urban environments. A sky-pointing camera is mounted on the top of a car and synchronized with a GNSS receiver. The advantages of this configuration are two-fold: firstly, for the GNSS signals, the upward-facing camera will be used to classify the acquired images into sky and non-sky (also known as segmentation). A satellite falling into the non-sky areas (e.g., buildings, trees) will be rejected and not considered for the final position solution computation. Secondly, the sky-pointing camera (with a field of view of about 90 degrees) is helpful for urban area ego-motion estimation in the sense that it does not see most of the moving objects (e.g., pedestrians, cars) and thus is able to estimate the ego-motion with fewer outliers than is typical with a forward-facing camera. The GNSS and visual information systems are tightly-coupled in a Kalman filter for the final position solution. Experimental results demonstrate the ability of the system to provide satisfactory navigation solutions and better accuracy than the GNSS-only and the loosely-coupled GNSS/vision, 20 percent and 82 percent (in the worst case) respectively, in a deep urban canyon, even in conditions with fewer than four GNSS satellites.
本文提出了一种将从朝上的摄像头估计的机器人或陆地车辆的自我运动与全球导航卫星系统（GNSS）信号相融合的方法，用于城市环境中的导航。一个指向天空的摄像头安装在汽车顶部，并与GNSS接收器同步。这种配置的优点有两方面：首先，对于GNSS信号，朝上的摄像头将用于将采集到的图像分类为天空和非天空（也称为分割）。落入非天空区域（例如建筑物、树木）的卫星将被排除，不用于最终位置解算计算。其次，指向天空的摄像头（视野约为90度）有助于在城市区域进行自我运动估计，因为它看不到大多数移动物体（例如行人、汽车），因此能够以比前置摄像头更少的异常值来估计自我运动。GNSS和视觉信息系统在卡尔曼滤波器中紧密耦合以获得最终位置解。实验结果表明，该系统能够提供令人满意的导航解决方案，并且在深度城市峡谷中，即使在少于四颗GNSS卫星的条件下，也比仅使用GNSS以及松耦合的GNSS/视觉系统分别具有更高的精度，在最坏情况下分别提高了20%和82%。
