Privacy-Preserved Visual Simultaneous Localization and Mapping Based on a Dual-Component Approach
Source abstract
Abstract
Edge-assisted visual simultaneous localization and mapping (SLAM) is widely used in autonomous driving, robot navigation, and augmented reality for environmental perception, map construction, and real-time positioning. However, it poses significant privacy risks, as input images may contain sensitive information, and generated 3D point clouds can reconstruct original scenes. To address these concerns, this paper proposes a dual-component privacy-preserving approach for visual SLAM. First, a privacy protection method for images is proposed, which combines object detection and image inpainting to protect privacy-sensitive information in images. Second, an encryption algorithm is introduced to convert 3D point cloud data into a 3D line cloud through dimensionality enhancement. Integrated with ORB-SLAM3, the proposed method is evaluated on the Oxford Robotcar and KITTI datasets. Results demonstrate that it effectively safeguards privacy-sensitive information while ORB-SLAM3 maintains accurate pose estimation in dynamic outdoor scenes. Furthermore, the encrypted line cloud prevents unauthorized attacks on recovering the original point cloud. This approach enhances privacy protection in visual SLAM and is expected to expand its potential applications.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robotics and Sensor-Based Localization · Advanced Image and Video Retrieval Techniques · 3D Surveying and Cultural Heritage
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China
Funding
National Natural Science Foundation of China · Natural Science Foundation of Guizhou Province
References (30)
Show source-provided references
1. Real-time performance-focused localization techniques for autonomous vehicle: A review10.1109/TITS.2021.3077800 ↗
2. Review of simultaneous localization and mapping (SLAM) for construction robotics applications10.1016/j.autcon.2024.105344 ↗
3. Navigating the landscape for real-time localization and mapping for robotics and virtual and augmented reality10.1109/JPROC.2018.2856739 ↗
4. Himmi, S., Ilter, O., Pailleau, F., Siegwart, R., Bescos, B., and Cadena, C. (2022, January 23–27). Don’t Share My Face: Privacy Preserving Inpainting for Visual Localization. Proceedings of the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan.10.1109/IROS47612.2022.9982235 ↗
5. Unnoticeable synthetic face replacement for image privacy protection10.1016/j.neucom.2021.06.061 ↗
6. Dosovitskiy, A., and Brox, T. (July, January 26). Inverting visual representations with convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.
7. Pittaluga, F., Koppal, S.J., Kang, S.B., and Sinha, S.N. (2019, January 15–20). Revealing scenes by inverting structure from motion reconstructions. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.10.1109/CVPR.2019.00023 ↗
8. Yang, K., Yau, J.H., Fei-Fei, L., Deng, J., and Russakovsky, O. (2022, January 17–23). A study of face obfuscation in imagenet. Proceedings of the 39th International Conference on Machine Learning (ICML), Baltimore, MD, USA.
9. McPherson, R., Shokri, R., and Shmatikov, V. (2016). Defeating image obfuscation with deep learning. arXiv.
10. Zhang, K., Luo, W., Zhong, Y., Ma, L., Stenger, B., Liu, W., and Li, H. (2020, January 13–19). Deblurring by realistic blurring. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.10.1109/CVPR42600.2020.00281 ↗
11. A novel chaos-based permutation for image encryption
12. Bian, S., Wang, T., Hiromoto, M., Shi, Y., and Sato, T. (2020, January 13–19). Ensei: Efficient secure inference via frequency-domain homomorphic convolution for privacy-preserving visual recognition. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.10.1109/CVPR42600.2020.00942 ↗
13. Speciale, P., Schonberger, J.L., Kang, S.B., Sinha, S.N., and Pollefeys, M. (2019, January 15–20). Privacy preserving image-based localization. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.10.1109/CVPR.2019.00564 ↗
14. Shibuya, M., Sumikura, S., and Sakurada, K. (2020, January 23–28). Privacy preserving visual slam. Proceedings of the Computer Vision–ECCV 2020, Glasgow, UK.10.1007/978-3-030-58542-6_7 ↗
15. Von Stumberg, L., Usenko, V., and Cremers, D. (2018, January 21–25). Direct sparse visual-inertial odometry using dynamic marginalization. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia.10.1109/ICRA.2018.8462905 ↗
16. Direct sparse odometry10.1109/TPAMI.2017.2658577 ↗
17. ORB-SLAM: A versatile and accurate monocular SLAM system10.1109/TRO.2015.2463671 ↗
18. Orb-slam3: An accurate open-source library for visual, visual–inertial, and multimap slam10.1109/TRO.2021.3075644 ↗
19. Edge assisted mobile semantic visual slam
20. Edge-SLAM: Edge-assisted visual simultaneous localization and mapping
21. He, K., Gkioxari, G., Dollár, P., and Girshick, R. (2017, January 22–29). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.10.1109/ICCV.2017.322 ↗
22. Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., and Huang, T.S. (November, January 27). Free-form image inpainting with gated convolution. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.
23. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. (2022, January 19–24). High-resolution image synthesis with latent diffusion models. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.10.1109/CVPR52688.2022.01042 ↗
24. Wang, C.Y., Liao, H.Y.M., Wu, Y.H., and Chen, P.Y. (2020, January 13–19). CSPNet: A new backbone that can enhance learning capability of CNN. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Seattle, WA, USA.10.1109/CVPRW50498.2020.00203 ↗
25. (2024, March 30). Architecture of yolov8. Available online: https://github.com/ultralytics/ultralytics/issues/189.
26. Nazeri, K., Ng, E., Joseph, T., Qureshi, F., and Ebrahimi, M. (November, January 27). Edgeconnect: Structure guided image inpainting using edge prediction. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, Seoul, Republic of Korea.
27. Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 21–26). Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.632 ↗
28. Zhao, Y., Lv, W., Xu, S., and Wei, J. (2024, January 16–22). Detrs beat yolos on real-time object detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.10.1109/CVPR52733.2024.01605 ↗
29. Farhadi, A., and Redmon, J. (2018, January 18–23). Yolov3: An incremental improvement. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.
30. Chelani, K., Kahl, F., and Sattler, T. (2021, January 20–25). How privacy-preserving are line clouds? recovering scene details from 3d lines. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.10.1109/CVPR46437.2021.01541 ↗