# Privacy-Preserved Visual Simultaneous Localization and Mapping Based on a Dual-Component Approach
## 6 Conclusions  
This paper proposes a novel privacy protection method that can improve the privacy protection performance of visual SLAM. Our method can automatically identify and realistically restore privacy-sensitive areas. In addition, point cloud data can be effectively converted into an unrecognizable line cloud form. Experimental results demonstrate that, first, after combining the methods, visual SLAM can accurately identify privacy-sensitive objects and restore the corresponding areas. Compared with other image privacy protection methods, this method makes privacy-sensitive information completely invisible, and the restored image is more conducive to subsequent positioning tasks. Secondly, the protected SLAM still guarantees excellent positioning accuracy. Finally, the encryption algorithm makes it difficult for attackers to recover accurate point cloud maps from line clouds.  
We noticed that the running time of the protected visual SLAM is slightly increased. However, the improvement in privacy protection performance is not negligible, which is conducive to broadening the application scope of visual SLAM. In future research, we intend to further improve our method, e.g., by making the detection model lightweight and deploying it in computationally constrained environments.  
Conceptualization, M.Y. and C.H.; methodology, M.Y. and C.H.; software, M.Y.; validation, M.Y.; formal analysis, M.Y.; investigation, M.Y. and X.H.; resources, S.H.; data curation, M.Y.; writing--original draft preparation, M.Y. and X.H.; writing--review and editing, M.Y. and C.H.; visualization, M.Y. and X.H.; supervision, M.Y. and C.H.; project administration, M.Y. and C.H.; funding acquisition, C.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number 62162007) and the Natural Science Foundation of Guizhou Province (grant number QianKeHeljChu-ZK[2024]YiBan079).  
Not applicable.  
Data Availability StatementRestrictions apply to the availability of the KITTI dataset. Data were obtained from Karlsruhe Institute of Technology and are available at [[https://www.cvlibs.net/datasets/kitti/eval_odometry.php](https://www.cvlibs.net/datasets/kitti/eval_odometry.php)]([https://www.cvlibs.net/datasets/kitti/eval_odometry.php](https://www.cvlibs.net/datasets/kitti/eval_odometry.php)) and [[https://www.cvlibs.net/datasets/kitti/raw_data.php](https://www.cvlibs.net/datasets/kitti/raw_data.php)]([https://www.cvlibs.net/datasets/kitti/raw_data.php](https://www.cvlibs.net/datasets/kitti/raw_data.php)) (accessed on 5 May 2024) with the permission of Karlsruhe Institute of Technology. Restrictions apply to the availability of the Oxford RobotCar dataset. The data were obtained from the University of Oxford and are available at [[https://robotcar-dataset.robots.ox.ac.uk/datasets/](https://robotcar-dataset.robots.ox.ac.uk/datasets/)]([https://robotcar-dataset.robots.ox.ac.uk/datasets/](https://robotcar-dataset.robots.ox.ac.uk/datasets/)) accessed on 5 May 2024 with the permission of the University of Oxford.  
The authors declare no conflicts of interest.