Outdoor Monocular SLAM with Global Scale-Consistent 3D Gaussian Pointmaps
This addresses robust 3D reconstruction and tracking for outdoor environments, but it is incremental as it builds on existing 3DGS SLAM methods with specific enhancements.
The paper tackled the problem of scale drift and lack of geometric priors in outdoor monocular SLAM using 3D Gaussian Splatting, proposing S3PO-GS which achieved state-of-the-art results in novel view synthesis and improved tracking accuracy on datasets like Waymo, KITTI, and DL3DV.
3D Gaussian Splatting (3DGS) has become a popular solution in SLAM due to its high-fidelity and real-time novel view synthesis performance. However, some previous 3DGS SLAM methods employ a differentiable rendering pipeline for tracking, lack geometric priors in outdoor scenes. Other approaches introduce separate tracking modules, but they accumulate errors with significant camera movement, leading to scale drift. To address these challenges, we propose a robust RGB-only outdoor 3DGS SLAM method: S3PO-GS. Technically, we establish a self-consistent tracking module anchored in the 3DGS pointmap, which avoids cumulative scale drift and achieves more precise and robust tracking with fewer iterations. Additionally, we design a patch-based pointmap dynamic mapping module, which introduces geometric priors while avoiding scale ambiguity. This significantly enhances tracking accuracy and the quality of scene reconstruction, making it particularly suitable for complex outdoor environments. Our experiments on the Waymo, KITTI, and DL3DV datasets demonstrate that S3PO-GS achieves state-of-the-art results in novel view synthesis and outperforms other 3DGS SLAM methods in tracking accuracy. Project page: https://3dagentworld.github.io/S3PO-GS/.