## 4 Results
Specifically, we outperform [36] by achieving an increase in accuracy of 5% and an improvement of 8% over [37]. In terms of processing speed, we achieve a remarkable speed of 91 fps, which is three times faster than [36]. However, ref. [37] exceeds our method with a processing speed advantage of 67 fps. Although [38] reports a 2% higher accuracy than our approach, it does so at the cost of processing speed. Their reported figure of 333.3 fps does not account for the intensive operations involved in image reading, feature extraction, and feature tracking, which are critical components in the visual odometry framework. Similarly, recent AI-based methods have been proposed to improve the performance of the proposed method. Figure 3: The algorithm’s performance was evaluated on the KITTI dataset’s training sequences, with results demonstrating consistently high accuracy across multiple test cases, closely matching the ground truth. These outcomes confirm that the model effectively captures vehicle motion and spatial consistency within diverse urban and suburban environments featured in KITTI. The blue line represents the ground truth, while the green line shows the estimated ego-motion using the proposed algorithm. methods, such as [29; 39; 40], achieve high accuracy levels but face significant limitations in processing speed. For example, ref. [40] reports an impressive accuracy of 98.3% but only 3 fps using a high-performance Nvidia Tesla V100 GPU, which makes it unsuitable for real-time applications. Furthermore, refs. [29; 39] achieve accuracies of 99.89% and 98.83%, respectively, but both operate at only 10 fps. In particular, ref. [39] relies solely on a CPU, which limits its computational efficiency in dynamic scenarios. In contrast, our proposed algorithm delivers a robust balance between accuracy (97.71%) and processing speed (91.78 fps) on a GPU platform, demonstrating its suitability for real-time performance in autonomous navigation tasks. In Figure 4, we present qualitative results for sequences 11-14 from the KITTI dataset. The results obtained by applying our proposed algorithm were submitted to the KITTI evaluation platform. In all cases, qualitative results demonstrate that high precision is possible, underscoring the effectiveness of the proposed algorithm in multiple urban environments. To further evaluate the robustness of the proposed algorithm, we applied it to a benchmark dataset consisting of indoor video sequences. This dataset, contains 130 different video sequences, each recording various movements recorded at six degrees of freedom (DOF). This indoor dataset offers a diverse set of movement scenarios, providing valuable insight into the performance of the algorithm under different conditions compared with the KITTI dataset. The results obtained from the indoor dataset, shown in Figure 5, demonstrate that the proposed method maintains high precision in both qualitative and quantitative terms. Despite the challenging indoor environment, where visual characteristics differ significantly from those in urban outdoor settings (as seen in the KITTI dataset), the algorithm consistently achieved strong performance across all test cases. For more details on the indoor benchmark dataset, including its creation and the full set of sequences used in this work, we refer the reader to the dataset webpage [41]. Concerning real-world disturbances, including motion blur, occlusions, and noisy sensor data, such factors are often present in dynamic environments where autonomous vehicles operate. In this scenario, our algorithm employs genetic algorithms, which are inherently adaptive and capable of selecting the best candidate solutions based on fitness criteria, such as image similarity between consecutive frames. This process allows the algorithm to effectively handle minor disturbances in the input data, as it can evolve potential movements even when certain frames are noisy or partially occluded. Moreover,