Integrating Global Navigation Satellite Systems (GNSS) in Simultaneous Localization and Mapping (SLAM) systems draws increasing attention to a global and continuous localization solution. Nonetheless, in dense urban environments, GNSS-based SLAM systems will suffer from the Non-Line-Of-Sight (NLOS) measurements, which might lead to a sharp deterioration in localization results. In this paper, we propose to detect the sky area from the up-looking camera to improve GNSS measurement reliability for more accurate position estimation. We present Sky-GVINS: a sky-aware GNSS-Visual-Inertial system based on a recent work called GVINS. Specifically, we adopt a global threshold method to segment the sky regions and non-sky regions in the fish-eye sky-pointing image and then project satellites to the image using the geometric relationship between satellites and the camera. After that, we reject satellites in non-sky regions to eliminate NLOS signals. We investigated various segmentation algorithms for sky detection and found that the Otsu algorithm reported the highest classification rate and computational efficiency, despite the algorithm's simplicity and ease of implementation. To evaluate the effectiveness of Sky-GVINS, we built a ground robot and conducted extensive real-world experiments on campus. Experimental results show that our method improves localization accuracy in both open areas and dense urban environments compared to the baseline method. Finally, we also conduct a detailed analysis and point out possible further directions for future research. For detailed information, visit our project website at https://github.com/SJTU-ViSYS/Sky-GVINS.

翻译：将全球导航卫星系统（GNSS）集成至同步定位与地图构建（SLAM）系统中，为提供全局且连续的定位解决方案引起了日益增长的关注。然而，在密集城市环境下，基于GNSS的SLAM系统会受到非视距（NLOS）测量的影响，这可能导致定位结果急剧恶化。本文提出通过朝上相机检测天空区域，以提高GNSS测量可靠性，进而实现更精确的位置估计。我们提出Sky-GVINS：一种基于最新工作GVINS的天空感知型GNSS-视觉-惯性系统。具体而言，我们采用全局阈值方法对鱼眼天空指向图像中的天空区域与非天空区域进行分割，随后利用卫星与相机之间的几何关系将卫星投影至图像。此后，我们剔除位于非天空区域的卫星，以消除NLOS信号。我们研究了多种用于天空检测的分割算法，发现大津算法尽管简单易实现，却报告了最高的分类率及计算效率。为评估Sky-GVINS的有效性，我们搭建了一款地面机器人，并在校园内开展了广泛的真实世界实验。实验结果表明，与基线方法相比，我们的方法在开阔区域及密集城市环境下均能提升定位精度。最后，我们还进行了详细分析，并指明了未来研究的可能方向。详细信息请访问我们的项目网站：https://github.com/SJTU-ViSYS/Sky-GVINS。