Sky-GVINS：一种面向城市峡谷鲁棒导航的天空分割辅助GNSS-视觉-惯性系统
Sky-GVINS: a Sky-segmentation Aided GNSS-Visual-Inertial System for Robust Navigation in Urban Canyons
- Shanghai Jiao Tong University（上海交通大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出Sky-GVINS，一种利用朝上相机检测天空区域以剔除NLOS卫星信号，从而提高城市峡谷中GNSS-视觉-惯性系统定位精度的鲁棒导航方法。
AI中文摘要：
将全球导航卫星系统（GNSS）集成到同步定位与地图构建（SLAM）系统中，因其能提供全局且连续的定位解决方案而日益受到关注。然而，在密集的城市环境中，基于GNSS的SLAM系统会遭受非视距（NLOS）测量问题，这可能导致定位结果急剧恶化。在本文中，我们提出从朝上相机中检测天空区域，以提高GNSS测量的可靠性，从而实现更精确的位置估计。我们提出了Sky-GVINS：一种基于近期工作GVINS的天空感知GNSS-视觉-惯性系统。具体而言，我们采用全局阈值方法在鱼眼朝上图像中分割天空区域和非天空区域，然后利用卫星与相机之间的几何关系将卫星投影到图像上。之后，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Otsu算法尽管简单且易于实现，但报告了最高的分类率和计算效率。为了评估Sky-GVINS的有效性，我们构建了一个地面机器人，并在校园内进行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和密集城市环境中均提高了定位精度。最后，我们还进行了详细分析，并指出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。
英文摘要：
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.
