## 5 Conclusions and Prospects  
This paper proposes a GNSS-RTK availability assessment method exploiting the registered 3D point map provided by LiDAR. Further integration of the available GNSS solutions, LiDAR, and IMU measurements are implemented, namely adaptive sensor fusion pipeline, to perform absolute position estimation under deep urban for autonomous navigation. The effectiveness of the GNSS-RTK availability assessment via mean elevation angle mask calculation of the local point cloud map is firstly verified according to the ATE of the GNSS measurements. For example, in most cases, a large mean elevation angle mask could be achieved under a sheltered environment. The GNSS would be assumed to be unavailable. Correspondingly, the ATE of the GNSS measurements tends to increase. With the help of the proposed GNSS-RTK selection method, poor GNSS solutions with large errors are excluded before the fused positioning. The trajectories estimated by such an adaptive fusion scheme are investigated quantitatively and qualitatively. During the experiments, different percentages of available GNSS solutions are leveraged as the threshold for availability assessment is tuned. The precision of the estimated trajectory is gradually improved as the percentage increases. In summary, the capabilities of the GNSS-RTK availability assessment module to avoid unavailable GNSS solutions and the adaptive fusion method to continuously estimate the position are demonstrated. However, the proposed method can be significantly challenged when the long-term GNSS interruption occurs. Specifically, the locally generated map will not be reliable for the GNSS-RTK selection due to the accumulated drift from the LiDAR/inertial odometry. Integrating more sensors to mitigate the potential drift of the LiDAR/inertial odometry will be one of the possible future research directions.  
For future studies, we would further improve the accuracy of the position estimation from meter levels to centimeter levels. The semantic map [64, 65, 66] with labeled objects would contribute to avoiding false GNSS unavailability assessment under open areas. Because the tall trees and other nearby objects which lead to large mean elevation angle masks could be noted and removed. Moreover, it can be seen from the integrated positioning results on the three urban canyons, that the achieved best positioning precisions are still meter levels. This is because all the GNSS-RTK solutions provided by the commercial level GNSS receiver are inaccurate due to the dense building structures. Therefore, GNSS solutions refinement via measurement model correction during deeper sensor fusion would be promising [67, 68]. Last but not least, the extrinsic parameters among different sensors make great sense [69]. Online calibration and adjustment are expected to be helpful [70, 71].  
Conceptualization, Y.W., L.-T.H., W.W. and J.Z.; methodology, W.W. and J.Z.; software, J.Z. and F.H.; validation, J.Z.; investigation, W.W. and J.Z.; resources, Y.W. and L.-T.H.; data curation, W.W., F.H. and J.Z.; writing--original draft preparation, J.Z.; writing--review and editing, W.W., L.-T.H. and J.Z.; supervision, [PERSON]-T.H. and X.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Riemann Laboratory, Huawei Technologies grant number ZGD2. The APC was also funded by Riemann Laboratory, Huawei Technologies. This research was also supported by the Faculty of Engineering, The Hong Kong Polytechnic University under the project \"Perception-based GNSS PPP-RTK/LVINS integrated navigation system for unmanned autonomous systems operating in urban canyons\". This research was also supported by the Guangdong Natural Science Foundation (2021A1515110771).  
We would like to thank our colleague [PERSON] for his kind help with the visualization of the mean elevation angel mask for GNSS-RTK solutions. The authors declare no conflict of interest.