论文标题
基于优化的视觉惯性猛击与RAW GNSS测量紧密结合
Optimization-Based Visual-Inertial SLAM Tightly Coupled with Raw GNSS Measurements
论文作者
论文摘要
与文献中的松散耦合方法和基于EKF的方法不同，我们提出了一种基于优化的视觉惯性猛击，并与原始的全球导航卫星系统（GNSS）测量紧密相结合，这是文献中此类的首次尝试。更具体地说，在滑动窗口内共同最大程度地减少了重新投影误差，IMU预一整合性误差和原始GNSS测量误差，其中图像和RAW GNSS测量之间的异步被解释了。此外，还解决了边缘化，嘈杂的测量结果以及解决弱势情况之类的问题。复杂城市场景中公共数据集的实验结果表明，我们提出的方法的表现优于最先进的视觉惯性大满贯，GNSS单点定位，以及一种松散的耦合方法，包括主要包含低层建筑物的场景和那些城市峡谷的场景。
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.
