突破户外RGB SLAM尺度漂移难题，精确定位+高保真重建（ICCV'25）

Core Viewpoint - The article discusses the innovative S3PO-GS framework developed by the Hong Kong University of Science and Technology (Guangzhou) to address the scale drift problem in outdoor monocular SLAM, achieving global scale consistency for RGB monocular SLAM [2][5][22]. Summary by Sections Introduction to SLAM - SLAM technology's robustness is crucial for performance in advanced fields such as autonomous driving, robot navigation, and AR/VR [3]. Challenges in Current SLAM Solutions - Existing 3D Gaussian-based SLAM solutions excel in indoor environments but struggle in unbounded outdoor settings due to the lack of depth prior in monocular systems, leading to geometric information insufficiency and scale drift issues [4][6]. S3PO-GS Framework - The S3PO-GS framework introduces three core technological breakthroughs: 1. A self-consistent tracking module that generates scale-consistent 3D point clouds and establishes accurate 2D-3D correspondences to eliminate drift errors in pose estimation [6]. 2. A dynamic mapping mechanism that employs a local patch-based scale alignment algorithm to dynamically calibrate the scale parameters of pre-trained point clouds with the 3D Gaussian scene [6]. 3. A joint optimization architecture that synchronously enhances localization accuracy and scene reconstruction quality through point cloud replacement strategies and geometric supervision loss functions [6]. Experimental Results - In benchmark tests on Waymo, KITTI, and DL3DV datasets, S3PO-GS demonstrated significant advantages, reducing tracking errors by 77.3% in the DL3DV scene and achieving a PSNR of 26.73 in the Waymo dataset, setting a new standard for real-time high-precision reconstruction in unbounded outdoor scenes [6][16][22]. Conclusion and Future Work - The S3PO-GS framework effectively addresses common issues of scale drift and geometric prior absence in outdoor scenes, reducing the number of iterations required for pose estimation to 10% of traditional methods [22][24]. Future research will explore loop detection and large-scale dynamic scene optimization to expand the application boundaries of this method in outdoor SLAM [24].