## 3 Results
### Comparative Analysis  
As mentioned in Section 2.3, we used the TUM-Mono dataset and benchmark because it has a complete set of metrics; all its sequences were gathered using only monocular cameras, and it presents the largest collection of 50 sequences and scenarios that comprise multiple outdoor and indoor examples. In addition, it must be considered that these sequences were gathered using a pure monocular handheld camera and were recorded by a walking person; so, the results presented in this section might not be generalized to considerably different applications like autonomous driving, flying drones, and medical exploration applications, among others. As addressed in the related works [6, 54], we followed the authors' suggestions of running each sequence of a benchmark five times to create cumulative-error plots and account for the nondeterministic nature of each system [40]. Nevertheless, authors like [30, 32] performed their experimental comparisons by running each sequence ten times in forward and backward reproduction directions to better capture the probabilistic behavior of the algorithms against multiple variations like illumination and dynamic objects. In this way, we applied this extended approach, given the large variety of algorithms we tested. In total, we performed ten runs of each of the 50 sequences in forward and backward modalities, gathering a total of 1000 runs for each method; so, for the ten evaluated algorithms, we created a database of 10,000 trajectory files saved in.txt format that were processed using the MATLAB scripts provided in the official repository of the TUM-Mono benchmark [6]. The TUM-Mono benchmark scripts require a specific structure, where each algorithm must output a.txt file containing all the camera poses registered during each sequence algorithm execution, where each \(\mathbf{P}_{i}\) pose must be in the quaternion format represented in Equation (1). However, the SVO, CNN-SVO, and DSM algorithms output rotation and translation matrixes instead of quaternions, which are incompatible with the TUM-Mono format. In this way, we had to modify the codes of these methods to introduce the correct output format, following the proposal of [PERSON] and [PERSON] [83], by applying Equations (11)-(15) to convert the translation and rotation outputs of the SVO, CNN-SVO, and DSM to quaternions. \[\mathbf{P}_{i}=\big{(}t_{i}\;x_{i}\;y_{i}\;z_{i}\;q_{x_{i}}\;q_{y_{i}}\,q_{z_{i}}\, q_{w_{i}}\big{)} \tag{11}\] \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Overall CPU Usage,** & **Current CPU Usage** & **GPU Usage** & **Memory Usage** & **Time** & **FPS** \\  & **Multicore** & **Usage** & **Usage** & **Usage** & **(s)** & **FPS** \\ \hline ORB-SLAM2 & 1.8472\% & 16.2374\% & 1.2376\% & 9.2147\% & 128.4571 & 37 \\ DF-ORB-SLAM & 1.9254\% & 17.4235\% & 1.7861\% & 12.4572\% & 133.1217 & 36 \\ LSD-SLAM & 2.4578\% & 18.4521\% & 1.6423\% & 10.3457\% & 138.4172 & 34 \\ DSO & 1.2604\% & 14.6818\% & 1.8971\% & 9.3892\% & 91.2564 & 52 \\ SVO & **1.1286\%** & **10.4859\%** & **1.7852\%** & **8.5316\%** & **87.5241** & **55** \\ LDSO & 1.6909\% & 15.4717\% & 3.1588\% & 14.2962\% & 99.4758 & 48 \\ DSM & 1.8346\% & 31.9216\% & 2.7203\% & 24.1591\% & 315.4982 & 15 \\ DynaSLAM & 1.9247\% & 21.4576\% & 15.3467\% & 20.3879\% & 118.3245 & 40 \\ CNN-DSO & 4.0647\% & 30.9091\% & 27.5346\% & 24.6742\% & 161.2389 & 30 \\ CNN-SVO & 3.2579\% & 27.8461\% & 24.4732\% & 23.5476\% & 134.7583 & 35 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The average CPU usage performance metrics for the ten executions of each algorithm on _sequence_01_ of the TUM-Mono dataset. Given the rotation matrix:  
\[