## 4 Discussion
### Performance of Adaptive GNSS-RTK/LIO Fusion  
* The continuous and smooth GNSS-RTK positioning under urban canyons with LIO. Under deep urban canyons, the positioning results of the GNSS-RTK are likely to be discontinuous between epochs due to poor signal transmission [63]. The ATE of the GNSS-RTK measurements is consequently large, as shown in the first row of Tables 1-3. With the assistance of LIO, the positioning accuracy of the integrated pipelines is improved by more than 50% compared with that of the GNSS-RTK alone as shown in the last three rows of Table 3 and the last four rows of Tables 4 and 5. What is more, the visualized trajectory is smooth and continuous as shown in Figures 7c, 9d and 11d. In conclusion, through integration with LIO, the continuous and smooth positioning of GNSS-RTK under deep urban canyons is realized. * The accumulated drift alleviation of LIO with GNSS-RTK. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Method** & \(\mathbf{el_{th}}\) (\({}^{\circ}\)) &
\begin{tabular}{c} **ATE (m)** \\ \end{tabular} \\ \cline{2-4}  & \multicolumn{1}{c}{Alone GNSS RTK} & - & 22.23 \\ \cline{2-4}  & LIO & - & 11.43 \\ \cline{2-4} Urban-3 & LIO-SAM & - & 38.18 \\ \cline{2-4}  & \multicolumn{1}{c}{} & 90 & 39.71 \\ Adaptive Integrated GNSS-RTK/LIO & \multicolumn{1}{c}{} & 35 & 29.28 \\  & \multicolumn{1}{c}{} & 20 & 13.43 \\  & \multicolumn{1}{c}{} & **15** & **10.02** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: ATE of the trajectory estimated by different pipelines on urabn-3. As local sensors, LiDAR and IMU could only provide relative motion estimations. Therefore, accumulated drift is unavoidable. Theoretically, in the fusion with the absolute positioning solutions from GNSS-RTK, the drift would be corrected. However, in our experiments on the three typical urban scenarios, the original GNSS-RTK solutions provided by the commercial level receivers are notably poor with tens of meters error as shown in the first row of Tables 3-5. For urban-1, no fixed GNSS-RTK solutions are provided. The RMSE of the ATE of the LIO is even slightly better than the best adaptive fusion positioning results, as shown in the second row and the last row of Table 3. On urban-2 and urban-3, with more fixed GNSS-RTK solutions developed, the RMSE of the ATE of the best adaptive fusion positioning results are better compared with that of LIO. The accumulated drift of LIO would be more alleviated as the quality of the GNSS-RTK measurements is improved. GNSS measurements refinement after selection would be a promising strategy for a further breakthrough. The superiority of the proposed adaptive GNSS-RTK/LIO integration. The proposed adaptive GNSS-RTK/LIO integration pipeline periodically performs a non-linear optimization-based fusion of GNSS-RTK and LIO, once the GNSS-RTK solution is assessed as available by the GNSS-RTK availability assessment module. As shown in the last three rows of Table 3 and the last four rows of Tables 4 and 5, the best precision achieved with the adaptive fusion pipeline is improved by more than 75% compared with that of the traditional method. The superiority of the proposed method is validated. By lowering the threshold for the assessment, more unavailable GNSS-RTK solutions would be rejected. The ATE of the positioning results is continuously improved. Moreover, the visualized trajectories are smoother as plotted in Figure 7a-c, Figures 7a-d and 71a-d. The effectiveness of the GNSS-RTK availability assessment is further demonstrated. Theoretically, the pipeline with the smallest threshold always achieves the best precision. However, in our experiments in the deep urban canyons with dense skyscrapers, when the threshold is smaller than 15\({}^{\circ}\) there is hardly any GNSS RTK solutions could be selected as available. In other words, 15\({}^{\circ}\) is the best threshold for the proposed method tested on deep urban canyons such as our UrbanNav dataset.