# A Distributed Indoor Mapping Method Based on Control-Network-Aided SLAM: Scheme and Analysis
## 3 Field Tests and Results
### Evaluation of Relative Accuracy and Absolute Accuracy  
Relative accuracy indicates the accuracy of the relative positional relationship between objects inside the map, which is an important indicator for evaluating the consistency of the mapping result in one SLAM process. We selected a certain number of feature points with the same name from the high-precision reference map and our map, then calculated the coordinate conversion parameters between the two sets of coordinate systems, and finally calculated the RMS (Root Mean Square) of  
Figure 4: Experimental site local environment.  
Figure 5: Control network distribution.  
the transformed coordinate residuals to evaluate the relative accuracy. We chose Block 1 to evaluate the relative accuracy of the control-network-aided SLAM. The mapping results are shown in Figure 6. The mapping result MAP_I is the result of adding the control network constraints to the back-end optimization. The mapping result MAP_II was constructed without considering any constraints. Loop closure was not considered in MAP_I or MAP_II. Table 1 shows the RMS of the coordinate error of the selected common feature points. The RMS values of the coordinate error of the selected common feature points of MAP_I and MAP_II were 0.08 m and 0.16 m, respectively. The mapping results show that adding control network constraints can effectively eliminate the cumulative error to improve the accuracy of the mapping result; the relative accuracy was improved by 49.8% compared to that of MAP_II. The accuracy of MAP_I reached the centimeter level without considering loop detection, which means that we can improve the accuracy of SLAM where it is difficult to detect loop closure by utilizing the method of adding control network constraints. The reason for this is that control point information includes the relative constraints between the submaps, which is similar to loop closure and can improve the accuracy of SLAM.  
Absolute accuracy indicates the consistency of the mapping results in different SLAM processes in the distributed mapping, which can be analyzed by the difference between the mapping results and the reference map in the control network coordinate system. It also refers to the accuracy of map stitching in various parts after distributed mapping. In order to analyze the absolute accuracy, the high-precision reference maps were converted into the control network coordinate system using target balls, which were placed artificially with known coordinates. The absolute position error of the same named points in the two maps which were in a unified coordinate system (high-precision reference map and MAP_I in Section 3.3) was calculated to analyze the absolute accuracy, which determined the accuracy of map stitching. We separately selected common feature points, such as corners or other obvious fixed objects, from MAP_I and the converted high-precision reference map, and then calculated the RMS of the absolute position errors to indicate the absolute accuracy. The RMS of the absolute position error of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mapping Result** & **Constraint Type** & **Number of Common Feature Points** & **RMS** \\ \hline MAP\_I & Control Network & 27 & 0.08 m \\ MAP\_II & No constraints & 27 & 0.16 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: RMS (Root Mean Square) comparison of different mapping results.  
Figure 6: **(a)** MAP_I: Mapping result of adding control network constraints; **(b)** MAP_III: Mapping result without control network or loop closure constraints.  
the selected common feature points was 0.13 m, which indicates that the mapping results of different blocks can achieve good splicing.