## 1 Introduction
Therefore, the integration of LIO and GNSS-RTK is a promising solution to provide locally accurate and globally referenced positioning. Specifically, the relative pose estimations from the LIO bridge the stage where GNSS is unavailable. The work in [34], [PERSON], [PERSON] loosely fused LiDAR scan-matching results, IMU pre-integration, and GNSS measurements via factor graph optimization (FGO). Its point-based probability grid scan-matching method was time-consuming [35]. The works in [36; 37], [PERSON], [PERSON], [PERSON], [PERSON], etc. employed GNSS/IMU to acquire geo-referencing of the LiDAR point cloud for further segmented obstacle localization which can help to improve the robustness of the LiDAR localization. Nonetheless, the integration of the GNSS-RTK is still an unsolved problem. In [38] conducted by [PERSON], Global Positioning System (GPS) and LiDAR are used alternatively to correct the observables of IMU via the EKF estimator. Interestingly, the work in [39], [PERSON], [PERSON] maintains two EKFs where one is for LiDAR and IMU providing velocities, and the other is for GNSS and IMU generating fused position estimates. The motion constraints, such as the zero-vertical-velocity and zero-lateral-velocity assumptions are adopted for the assessment of the pseudorange from GNSS. However, turns and slopes are common during driving which disproves the assumptions. Based on error-state EKF, the work in [40], [PERSON], [PERSON] loosely fused GNSS/IMU/LiDAR with a pre-built map and operated on high-end equipment. Relative motion predictions from IMU and LiDAR contribute to resolving ambiguities when GNSS signals are reacquired. Unfortunately, KF-based methods could not fully consider historical information simultaneously due to the first-order of the Markov chain, and iterated linearization is not performed resulting in limited precision [41]. In short, all the above-mentioned methods brutely fused all the received GNSS measurements. Although the reliability of each measurement is characterized [42], the error incurred by seriously wrong measurements could not be eliminated as possible. Given the fact that the existing LIO can provide accurate relative positioning in a short period which is shown in our previous work [33], can we only periodically select the good GNSS-RTK for effective fusion? More importantly, the performance of the GNSS-RTK relies heavily on the environment structures and the LIO can effectively reconstruct the surroundings using 3D point clouds. Inspired by this exciting feature and to fully leverage the merits of multi-sensors, this paper proposes an adaptive GNSS-RTK/LIO integration scheme by selecting the reliable GNSS-RTK solutions based on the incrementally generated 3D point clouds map from LIO. Therefore, during the GNSS-RTK/LIO integration, the divergence caused by abnormal GNSS solutions is eradicated from the source. Meanwhile, smoothness and continuity are ensured by the LIO. The drift of the 3D point cloud map is corrected by the selected reliable GNSS-RTK solutions. In conclusion, the contributions of this paper are highlighted as follows:  
* This paper proposed a GNSS-RTK availability assessment method, during which, unreliable GNSS measurements rejection is performed through mean elevation angle mask evaluation exploiting the registered 3D map that was incrementally constructed by LIO based on our previous work in [33]. It is worth mentioning that the mean elevation angle in this paper is defined as that generated by the buildings around the GNSS receiver rather than the satellites. * This paper proposed a GNSS-RTK/LIO integration scheme based on factor graph optimization. Concretely, the globally referenced positioning from reliable GNSS-RTK and relative pose estimation are integrated using the FGO [24] which is free of abnormal GNSS-RTK solutions. * The effectiveness of the proposed GNSS-RTK availability assessment and the adaptive sensor fusion is validated on three typical urban canyons datasets collected in Hong Kong [9].