## 3 Results  
### Experimental Setup  
**Sensor setups:** To validate the effectiveness of the proposed method, the experiment is conducted on three datasets collected in typical urban canyons in Hong Kong. As shown in Figure 4a,b, the Velodyne HDL-32E was employed to collect 3D point clouds at a frequency of 10 Hz. The Xsens Ti-10 IMU was employed to collect acceleration and angular velocity at a frequency of 200 Hz. A commercial-level u-blox GNSS receiver was used to collect the raw GNSS measurements, based on which the GNSS-RTK solution was further calculated by RTKLIB [7]. Besides, the NovAtel SPAN-CPT, a GNSS (GPS, GLONASS, and Beidou) RTK/INS (fiber-optic gyroscopes, FOG) integrated navigation system was used to provide the ground truth. In the SPAN-CPT, the high-end INS are integrated with GNSS to ensure continuous position estimation during GNSS outage. The NovAtel Inertial Explorer is also exploited to deliver precise positioning results. The ground truth provided by the SPAN is reliable and precise enough for evaluation. All the data were collected and synchronized using ROS [60]. The extrinsic parameters among all the sensors were calibrated in advance. Table 1 summarizes the three datasets on which the framework proposed in this paper is verified, namely UrbanNav-HK-Data20190428, UrbanNav-HK-Deep-Urban-1, and UrbanNav-HK-Data202010714, abbreviated as urban-1, urban-2, and urban-3 respectively in the following. The first two data are contained in our recently open-sourced UrbanNav dataset [9]. As shown in Figure 5a, urban-1 is collected at Tsim Sha Tsui (an urban scenario with buildings and trees in Hong Kong) with a total length of 2.01 km. As shown in Figure 5b, urban-2 is collected at Whampoa (an urban scenario with dense buildings in Hong Kong) from which the last 2.37 km is selected for the experiment. As shown in Figure 5c, urban-3 is collected from Whampoa to Tsim Sha Tsui with a much longer length of 4.3 km. The settings specified in the RTKLIB are listed in Table 2. The settings specified in the RTKLIB when processing the raw GNSS measurements from u-blox to achieve GNSS-RTK solutions for the three datasets are listed in Table 2 respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Length (km)** & **Urbanization** &
\begin{tabular}{c} **U-Blox** \\ **RECEIVER** \\ \end{tabular} \\ \hline UrbanNav-HK-Data20190428 [9] & 2.01 & Medium & MST \\ UrbanNav-HK-Deep-Urban-1 [9] & 2.37 & Deep & MST \\ UrbanNav-HK-Data20210714 1 & 4.3 & Medium & F9P \\ (urban-3) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The information of the three datasets used for experimental verification in this paper. Figure 4: Sensor setup deployment during the experimental data collection. (**a**). GNSS antennas of NovAtel SPAN-CPT and u-blox M8t. (**b**). The Xsens Ti-10 IMU and the Velodyne HDL-32E LiDAR. **Evaluation metrics:** The validity of the GNSS-RTK availability assessment method and the adaptive GNSS-RTK/LIO fusion pipeline for positioning are investigated. The 3D absolute translation error (ATE) implemented in EVO [61] is defined as follows:  
\[e_{k}=\|^{n}\mathbf{p}_{k,est}-^{n}\mathbf{p}_{k,gt}\|_{2}, \tag{19}\]  
In which, \({}^{n}\mathbf{p}_{k,est}\in\mathbb{R}^{3}\) and \({}^{n}\mathbf{p}_{k,gt}\in\mathbb{R}^{3}\) represents the estimated position and the ground truth of the \(k\)-th epoch respectively. \(\|\cdot\|_{2}\) denotes the 2-norm. \(e_{k}\) is the ATE of \({}^{n}\mathbf{p}_{k,est}\). The quantitative comparison of the estimated position from different pipelines is performed by comparing the Root Mean Square Error (RMSE) of ATE to the total traveled length. **Evaluated methods**: To verify the contributions of the proposed method in this paper, we evaluated the following methods. * _Method_**1**: This indicates the conventional GNSS/LIO fusion method [34; 42; 62] that directly integrates all the received GNSS-RTK measurements, among which LIO-SAM [42] is open-sourced.