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Integrating Global Navigation Satellite Systems (GNSS) in SimultaneousLocalization and Mapping (SLAM) systems draws increasing attention to a globaland 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 localizationresults. In this paper, we propose to detect the sky area from the up-lookingcamera to improve GNSS measurement reliability for more accurate positionestimation. We present Sky-GVINS: a sky-aware GNSS-Visual-Inertial system basedon a recent work called GVINS. Specifically, we adopt a global threshold methodto segment the sky regions and non-sky regions in the fish-eye sky-pointingimage and then project satellites to the image using the geometric relationshipbetween satellites and the camera. After that, we reject satellites in non-skyregions to eliminate NLOS signals. We investigated various segmentationalgorithms for sky detection and found that the Otsu algorithm reported thehighest classification rate and computational efficiency, despite thealgorithm's simplicity and ease of implementation. To evaluate theeffectiveness of Sky-GVINS, we built a ground robot and conducted extensivereal-world experiments on campus. Experimental results show that our methodimproves localization accuracy in both open areas and dense urban environmentscompared to the baseline method. Finally, we also conduct a detailed analysisand point out possible further directions for future research. For detailedinformation, visit our project website athttps://github.com/SJTU-ViSYS/Sky-GVINS.

Sky-GVINS: a Sky-segmentation Aided GNSS-Visual-Inertial System for Robust Navigation in Urban Canyons