## 4 Results  
For all experiments, we tested our algorithm on an MSI Raider GE76 12U laptop equipped with an Intel Core i7-12700H CPU and a GTX 3080 laptop GPU with 7424 CUDA cores and a maximum of 1024 threads per block (see Table 1). All experiments were performed in MATLAB 2022a, utilizing CUDA 8.1 as the GPU processing library. The dataset used was KITTI [31], a well-known benchmark for visual odometry challenge. This dataset was created using a stereo camera mounted on a vehicle navigating through urban environments. The images have a resolution of \(1241\times 376\) pixels with a frame rate of 10 fps. For evaluation, KITTI provides 11 training sequences (00-10) with public ground truth data, while an additional 11 sequences (11-21) lack public ground truth and are reserved for evaluation purposes. First, we evaluated cross-validation across the training sequences by constructing the lookup table (LUT Section 3.3) using all sequences from the training dataset, excluding the sequence under evaluation (see Table 2). This approach enables the assessment of the generalization of the model in different driving scenarios. Given the provided LUTs and their associated ground truth values, we have access to 21,732 elements in \(M_{k}\) (Equation (1)). We chose a LUT length of 100, capturing the 100 most representative movements, which yielded approximately 97% accuracy while achieving an ego-motion estimation of 81 frames per second. Figure 3 presents qualitative results corresponding to the data outlined in Table 2, illustrating the algorithm's capacity to adapt to multiple conditions in different driving environments. These cross-validation findings suggest that our approach effectively balances speed and accuracy, a balance that is particularly advantageous for real-time applications in dynamic environments, such as autonomous driving. Moreover, by focusing on the most significant movements within the LUT, our approach ensures robust ego-motion estimation without requiring extensive computational resources, making it suitable for embedded implementations in autonomous vehicle scenarios. In addition, we conducted a comparative performance analysis with previous work in the current literature. For these comparisons, performance metrics from previously published manuscripts were referenced. For our algorithm, we submit the results of the test sequences (11-21) to the KITTI benchmark suite and then compare the results obtained with previous work. Quantitative comparisons are presented in Table 3. Our algorithm demonstrates higher performance compared with traditional methods like [33; 34], outperforming both accuracy and processing speed. This advantage lies in the fact that many conventional approaches rely on binary feature description and matching techniques, which tend to be sensitive to image quality degradation. In contrast, the genetic processes proposed in this work enhance robustness against such degradations, leading to improved overall performance. \begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Value** \\ \hline Input data size (independent) & \([x,y]\) \\ Output data size (independent) & \([x,y]\) \\ Threads per block (independent) * & \(T\) \\ Blocks size (dependent) & \([\text{floor}(\sqrt{T}),\text{floor}(\sqrt{T}),1]\) \\ Grid size (dependent) & \([\text{floor}(x/\sqrt{T}),\text{floor}(y/\sqrt{T}),1]\) \\ \hline \hline \end{tabular}  
* This value depends on the GPU device; the most typical value is \(T=1024\).  
\end{table} 
Table 1: Thread configuration for the GPU implementation. \begin{table}
\begin{tabular}{c c} \hline \hline
**KITTI Sequence** & **Accuracy** \\ \hline
00 & 96.77\% \\
01 & 95.67\% \\
02 & 98.23\% \\
03 & 97.56\% \\
04 & 96.75\% \\
05 & 98.01\% \\
06 & 96.24\% \\
07 & 97.34\% \\
08 & 97.21\% \\
09 & 96.76\% \\
10 & 98.11\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative results for the KITTI dataset. Training sequences 00–10. Compared with [35; 36; 37; 38], our algorithm achieves a good trade-off between accuracy and processing speed.