# Privacy-Preserved Visual Simultaneous Localization and Mapping Based on a Dual-Component Approach
## 1 Introduction  
Visual simultaneous localization and mapping (SLAM) utilizes images as the primary source of environmental perception, estimating camera pose and trajectory through feature extraction and matching while reconstructing a map of the surrounding scene. Edge computing offloads computationally intensive steps, enhancing accessibility and expanding the applications of visual SLAM. Edge-assisted visual SLAM is essential for autonomous driving [1], robot navigation [2], and virtual/augmented reality (AR/VR) [3]. However, transmitting images to edge servers introduces privacy risks [4], as their leakage could lead to significant and unpredictable consequences [5]. Moreover, the generated three-dimensional (3D) point cloud can reconstruct the original scene [6, 7], further exacerbating privacy concerns. Thus, privacy protection remains a critical challenge in edge-assisted visual SLAM.  
To address this challenge, researchers have proposed various privacy-preserving methods to protect the privacy of images, such as introducing noise or applying image blurring [8, 9]. However, these approaches degrade global and local feature descriptors, and processed images can still be restored using image deblurring techniques [4, 10]. Other methods focus on encrypting images [11, 12], but encrypted images require decryption forpose estimation, increasing the transmission overhead in visual SLAM. Image inpainting is an image processing technique that fills or repairs missing or requested areas in an image to make it look visually natural and coherent. This technology is considered promising for alleviating the privacy protection issues of visual SLAM.  
For point clouds, [PERSON] et al. [13] proposed lifting 2D/3D feature points into random 2D/3D lines during visual localization, preserving privacy while ensuring accurate camera pose estimation. Building on this idea, [PERSON] et al. [14] developed a visual SLAM framework that integrates hybrid point-line features. It realizes efficient relocation and tracking while maintaining privacy during computations. However, this method [14] primarily protects point clouds during processing. After computation, line clouds are discretized into point clouds, leaving the generated 3D point cloud map unprotected. Additionally, it does not safeguard input images.  
Existing privacy protection schemes for visual SLAM primarily safeguard point clouds during runtime but overlook the privacy-sensitive information in incoming images and the output 3D map. This gap underscores the need for a method that protects both while maintaining SLAM pose estimation performance. To address this, this paper introduces a dual-component approach that combines the inpainting of privacy-sensitive areas in images with the encryption of 3D point clouds. This method aims to enhance privacy preservation in edge-assisted visual SLAM. The main contributions of this paper are as follows:  
1. We propose a dual-component privacy protection scheme for visual SLAM, which integrates the protection of both image and point cloud data.
2. We propose an image inpainting method for privacy-sensitive areas. This method integrates object detection based on multi-scale feature fusion with two-stage image inpainting based on edge prediction and image inpainting. It conceals privacy-sensitive information from input images and accurately restores the affected areas.
3. We introduce an encryption algorithm for point clouds based on the concept of dimensionality lifting. By randomly determining the direction and normalizing the length, each 3D point can be encrypted into a 3D line to obtain a 3D line cloud, improving the anti-attack ability of the visual SLAM output map.