## 5 Conclusions  
This study undertook a thorough assessment of SLAM algorithms in both indoor and outdoor environments, leveraging state-of-the-art techniques. Two pivotal SLAM algorithms, namely ORB-SLAM3 and SC-LeGO-LOAM, were utilized, along with the ground truth creation method Cartographer. Additionally, two 3D reconstruction methods employing RGB-D and LiDAR point clouds were employed. The experiments entailed the collection of real-world datasets across varied settings, with a focus on analyzing the algorithms' efficacy in terms of localization, mapping, and computational resource demands. The ensuing findings are derived from the outcomes:  
* In terms of localization performance, ORB-SLAM3 exhibits superior positioning accuracy in indoor environments, while SC-LeGO-LOAM excels in outdoor settings. This distinction arises from the nature of SLAM algorithms, which benefit from environmental intricacies, making it easier for the robot to calculate and match features;
* In terms of mapping capability, the effectiveness of mapping is closely tied to the sensor's characteristics and each has its unique drawbacks. For instance, SLAM mapping using depth cameras may suffer from imperfections and blind spots due to indoor lighting sources and limitations on FOV, respectively. Three-dimensional LiDAR-based \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scenario 1** & \multicolumn{2}{c}{**Scenario 2**} & \multicolumn{2}{c}{**Scenario 3**} \\ \hline
**In Terms of CPU Usage** & \multicolumn{2}{c}{**In Terms of CPU Usage**} & \multicolumn{2}{c}{**In Terms of Memory**} \\
**in User** & \multicolumn{2}{c}{**in User**} & \multicolumn{2}{c}{**Increasing Ratio**} \\ \hline ORB\_1 vs. SC\_I & 51.25 & ORB\_1 vs. Org & 77.80 & ORB\_1 & 22.18 \\ \hline ORB\_O vs. SC\_O & 35.02 & ORB\_O vs. Org & 70.67 & ORB\_O & 12.12 \\ \hline \hline  & SC\_1 vs. Org & 17.55 & SC\_I & 2.40 \\ \hline  & SC\_O vs. Org & 26.41 & SC\_O & 2.68 \\ \hline \hline \end{tabular}  
* The comparison of A to B refers to the latter, B. The notations carry the same meaning as those in Table 3.  
\end{table} 
Table 4: Three comparisons among different perspectives * (Unit %). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**CPU Utilization**} & \multicolumn{3}{c}{**Memory Usage**} \\  & **User** & **Sys** & **Idle** & **Occupancy Rate** & **Increasing Ratio** \\ \hline Org & 14.93 & 3.03 & 73.08 & 27.20 & NA \\ \hline ORB\_I & 26.54 & 3.13 & 60.83 & 34.20 & 22.18 \\ \hline ORB\_O & 25.48 & 3.20 & 61.80 & 33.69 & 12.12 \\ \hline SC\_I & 17.55 & 3.08 & 70.25 & 31.49 & 2.40 \\ \hline SC\_O & 18.87 & 3.32 & 68.45 & 30.42 & 2.68 \\ \hline \hline \end{tabular}  
* Note that the notation ‘Org’ denotes the average CPU usage in User without SLAM algorithm running. ‘ORB_I’, ‘ORB_O’, ‘SC_I’, and ‘SC_O’ mean the average amount of CPU time spent in user mode indoors using the ORB algorithm, outdoors using the ORB algorithm, indoors using SC-LeGO-LOAM algorithm, and outdoors using SC-LeGO-LOAM algorithm, respectively.  
\end{table} 
Table 3: Average results for the CPU utilization under various types of task and memory usage increasing ratio * (Unit: %). mapping may lack the level of detail and textural information required for precise object recognition. Nevertheless, in accordance with the distinct mapping spatial scopes between indoor and outdoor environments, LiDAR SLAM is more suitable in an outdoor environment due to its comprehensive characteristics. In contrast, Visual SLAM is more suitable in an indoor environment due to its recognizability characteristic and ability to compensate for the sparse aspects inherent in LiDAR SLAM;
* Regarding performance evaluation, quantitative analysis of computational resource requirements indicates that Visual SLAM demands more CPU resources compared to LiDAR SLAM, attributing this to the storage of additional depth and color data. The study also explores CPU utilization and memory usage increase ratios, emphasizing the impact of environmental factors on resource demands.