# Privacy-Preserved Visual Simultaneous Localization and Mapping Based on a Dual-Component Approach
## 4 Results
### Accuracy of Pose Estimation  
Experiments were conducted on the original image datasets from KITTI and Oxford Robotcar. Specifically, we compared the pose estimation performance of three kinds of approaches: the original ORB-SLAM3, ORB-SLAM3 with various image privacy protection methods, and ORB-SLAM3 integrated with the privacy protection method proposed in this paper. This comparison allowed us to evaluate the impact of the proposed method on pose estimation accuracy in visual SLAM. Since the KITTI and Oxford Robotcar datasets do not provide ground truth for ORB-SLAM3, the pose estimation results from the original ORB-SLAM3 were used as the evaluation benchmark. To quantify the impact of different privacy protection methods, the root mean square error (RMSE) of the absolute trajectory error across different sequences was compared. The reported results represent the median values obtained from 10 independent trials. Additionally, experiments were conducted on the KITTI SLAM Evaluation 2012 dataset, which contains long-range sequences that effectively assess the pose estimation performance of SLAM. This dataset is particularly valuable as trajectory visualization helps identify anomalies such as drift errors and pose outliers, enabling a more comprehensive analysis of SLAM performance. Unlike the previous datasets, the KITTI SLAM Evaluation 2012 dataset provides ground truth trajectories for each sequence, which were used as the evaluation benchmark. All other experimental settings remained consistent with the previous experiments. As shown in Table 4, on the KITTI and Oxford Robotcar datasets, the dual-component approach enables ORB-SLAM3 to achieve more accurate pose estimation with lower RMSE across all sequences, compared to other privacy-preservation methods. This indicates that our approach better maintains localization accuracy in visual SLAM. By considering the surrounding scenes when inpainting privacy-sensitive areas, our method generates more natural inpainted images. In contrast to other methods, the inpainted areas produced by our approach offer more feature points, enhancing ORB-SLAM3's pose estimation capability. In comparison, other methods hindered feature extraction due to their handling of privacy-sensitive areas. Complete masking with uniform pixels made it difficult to extract useful feature points, while noise addition interfered with feature point detection, reducing pose estimation performance. However, in sequence 58, the performance of the dual-component approach declined due to significant illumination variations, as shown in Figure 7, which affected ORB-SLAM3's accuracy. Other methods also experienced performance degradation in this sequence. Compared to other privacy-preserving methods, our approach reduces the average RMSE by 58% on the Oxford Robotcar dataset and 69.18% on the KITTI dataset, highlighting its effectiveness in maintaining localization accuracy. The experimental results on the KITTI SLAM Evaluation 2012 dataset are presented in Table 5. Compared to other privacy protection methods, the approach proposed in this paper effectively guarantees the positioning accuracy of ORB-SLAM3, with the smallest RMSE increase in absolute pose error. Moreover, the pose estimation accuracy of ORB-SLAM3 with privacy protection remains close to that of the original ORB-SLAM3. Notably, in certain sequences, ORB-SLAM3 integrated with our method even outperforms the original version. Given that this dataset represents large outdoor scenes with moving pedestriansand vehicles, these results demonstrate the potential and practicality of the proposed method for real-world autonomous driving scenarios and augmented reality applications. To evaluate the impact of the proposed dual-component privacy protection method on ORB-SLAM3's positioning accuracy, Figure 8 compares the pose trajectories of the original and protected versions of ORB-SLAM3. Figure 8a,c compare pose estimation results for sequence 12 of the Oxford Robotcar dataset and sequence 001 of the KITTI dataset.