# Performance of Different SLAM Algorithms for Indoor and Outdoor Mapping Applications
## 4 Results and Conclusions  
In this study, to determine the accuracies of the A-LOAM and HDL Graph SLAM algorithms, the distances obtained in the final 3D maps were compared with their actual values, and the results are given in Tables 1 and 2 for indoor and outdoor environments respectively. Actual values refer to direct on-site measurement results.  
In Table 3, mean error and standard deviation values of the results are given, indicating that the accuracy of the A-LOAM results is two times more accurate than HDL Graph SLAM results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Actual Value** & **A-LOAM Result** & **HDL Graph SLAM Result** & **A-LOAM Error** & **HDL Graph SLAM Error** \\ (**m**) & **(m)** & **(m)** & **(cm)** & **(cm)** \\ \hline
60.476 & 60.458 & 60.381 & \(-1.8\) & \(-9.5\) \\
13.945 & 13.983 & 13.909 & 3.8 & \(-3.6\) \\
8.262 & 8.249 & 8.304 & \(-1.3\) & 4.2 \\
4.081 & 4.108 & 4.128 & 2.7 & 4.7 \\
1.705 & 1.712 & 1.713 & 0.7 & 0.8 \\
1.592 & 1.623 & 1.649 & 3.1 & 5.7 \\
1.279 & 1.272 & 1.232 & \(-0.7\) & \(-4.7\) \\
1.263 & 1.254 & 1.239 & \(-0.9\) & \(-2.4\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Differences between the actual values and the SLAM Results for indoor environment.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Actual Value** & **A-LOAM Result** & **HDL Graph SLAM Result** & **A-LOAM Error** & **HDL Graph SLAM Error** \\
**(m)** & **(m)** & **(m)** & **(cm)** & **(cm)** \\ \hline
98.643 & 98.628 & 98.701 & \(-1.5\) & 5.8 \\
75.164 & 75.141 & 75.115 & \(-2.3\) & \(-4.9\) \\
40.487 & 40.503 & 40.529 & 1.6 & 4.2 \\
12.762 & 12.786 & 12.734 & 2.4 & \(-2.8\) \\
3.267 & 3.252 & 3.242 & \(-1.5\) & \(-2.5\) \\
1.452 & 1.443 & 1.487 & \(-0.9\) & 3.5 \\
0.408 & 0.419 & 0.435 & 1.1 & 2.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Differences between the actual values and the SLAM Results for an outdoor environment.  
Figure 7: Corrected final 3D Maps: (**a**) top view of the indoor environment; (**b**) perspective view of the indoor environment; (**c**) top view of the outdoor environment; (**d**) perspective view of the outdoor environment.  
This study aimed to test the performances of different SLAM algorithms in 3D mapping studies in indoor and outdoor environments. Test results show that the LOAM algorithm was less accurate than the A-LOAM and HDL Graph SLAM algorithms in both indoor and outdoor environments. However, the LOAM algorithm may give better results with more powerful computer hardware. The processing times of both the A-LOAM and HDL Graph SLAM algorithms are approximately equal, depending on the size and level of detail in the studied area. However, in order to obtain high accuracy results with the LOAM algorithm, it is recommended that the processor capacity of the computer used should be at least Intel i7 or equivalent.  
In addition, loop closure and plane detection methods were applied to the 3D maps obtained with A-LOAM and HDL Graph SLAM algorithms. It has been determined that the odometry drift, which occurred especially on long lines and long corridor environments, can be largely eliminated by manual and automatic loop closure methods. Also, it has been determined that the plane detection method after the automatic loop closure method improves the results significantly, especially in indoor environments.  
Additional sensors such as IMU and GNSS were not used in this study. The SLAM algorithms could provide more accurate results with additional sensors; therefore, further studies should be conducted to evaluate these possibilities. In addition, the Velodyne VLP-16 LIDAR sensor was used in the mobile mapping system in the study. By increasing the number of sensors to 2, more points related to the measured environment will be included in the SLAM algorithms, and the resulting 3D map will be more accurate.  
This research received no external funding.  
The authors declare no conflict of interest.