Unlike loose coupling approaches and the EKF-based approaches in the literature, we propose an optimization-based visual-inertial SLAM tightly coupled with raw Global Navigation Satellite System (GNSS) measurements, a first attempt of this kind in the literature to our knowledge. More specifically, reprojection error, IMU pre-integration error and raw GNSS measurement error are jointly minimized within a sliding window, in which the asynchronism between images and raw GNSS measurements is accounted for. In addition, issues such as marginalization, noisy measurements removal, as well as tackling vulnerable situations are also addressed. Experimental results on public dataset in complex urban scenes show that our proposed approach outperforms state-of-the-art visual-inertial SLAM, GNSS single point positioning, as well as a loose coupling approach, including scenes mainly containing low-rise buildings and those containing urban canyons.

翻译：与文献中松散的混合方法和基于EKF的方法不同,我们建议采用最优化的视觉-光纤SLM,紧紧结合原始全球导航卫星系统(GNSS)测量,这是文献中我们了解的首次尝试,更具体地说,在滑动窗口中,将回射错误、IMU前整合错误和原全球导航卫星系统测量错误联合减到最低程度,在滑动窗口中,图像和原全球导航卫星系统测量结果之间的不同步现象得到了考虑。此外,还解决了边缘化、噪音测量除去和处理脆弱情况等问题。在复杂的城市景象中公共数据集的实验结果显示,我们拟议的方法优于最新水平的视觉-光学 SLISM、全球导航卫星系统单点定位以及松散的组合方法,包括主要包含低层建筑物和含有城市峡谷的场景。