RGB-Only Gaussian Splatting SLAM for Unbounded Outdoor Scenes (2502.15633v1)

Abstract: 3D Gaussian Splatting (3DGS) has become a popular solution in SLAM, as it can produce high-fidelity novel views. However, previous GS-based methods primarily target indoor scenes and rely on RGB-D sensors or pre-trained depth estimation models, hence underperforming in outdoor scenarios. To address this issue, we propose a RGB-only gaussian splatting SLAM method for unbounded outdoor scenes--OpenGS-SLAM. Technically, we first employ a pointmap regression network to generate consistent pointmaps between frames for pose estimation. Compared to commonly used depth maps, pointmaps include spatial relationships and scene geometry across multiple views, enabling robust camera pose estimation. Then, we propose integrating the estimated camera poses with 3DGS rendering as an end-to-end differentiable pipeline. Our method achieves simultaneous optimization of camera poses and 3DGS scene parameters, significantly enhancing system tracking accuracy. Specifically, we also design an adaptive scale mapper for the pointmap regression network, which provides more accurate pointmap mapping to the 3DGS map representation. Our experiments on the Waymo dataset demonstrate that OpenGS-SLAM reduces tracking error to 9.8\% of previous 3DGS methods, and achieves state-of-the-art results in novel view synthesis. Project Page: https://3dagentworld.github.io/opengs-slam/

Insights into RGB-Only Gaussian Splatting SLAM for Unbounded Outdoor Scenes

The presented paper introduces an innovative RGB-only approach to Simultaneous Localization and Mapping (SLAM) for outdoor environments using a method termed OpenGS-SLAM. This approach tackles the limitations of previous Gaussian Splatting (3DGS)-based SLAM methodologies, which largely depend on depth inputs or pre-trained depth estimation models and mainly focus on constrained indoor settings. OpenGS-SLAM delineates a clear path forward by enabling robust SLAM functionalities in unbounded, outdoor scenarios with merely RGB data.

Technical Contributions

1. Pointmap Regression for Pose Estimation: The paper delineates a sophisticated pointmap regression network, designed to produce consistent pointmaps from frame sequences. Pointmaps inherently encapsulate 3D structures from multiple standard views, thereby supporting reliable camera pose estimation. This capability is pivotal for rendering processes when depth information is unavailable, a generally challenging scenario for outdoor areas given their complicated geometric structuring and viewing angles.
2. Integration into Differentiable Pipeline: The end-to-end differentiable pipeline employed integrates camera pose estimation with 3DGS rendering, providing synchronized optimization of camera poses and 3D Gaussian splatting scene parameters. This simultaneous optimization enhances the robustness of camera tracking and the reliability of reconstructed scenes.
3. Scale Mapper Design: The methodology introduces an adaptive scale mapper within the pointmap regression network architectures, facilitating accurate scaling and mapping of pointmaps to the resultant 3DGS map representation. This feature is essential for mitigating scale estimation inaccuracies, which could otherwise propagate errors throughout the SLAM process.

Experimental Evaluation and Results

The method's efficacy is showcased through experiments on the Waymo dataset, noted for its relevance in autonomous vehicle research. OpenGS-SLAM excels by reducing tracking error to 9.8% of what prior 3DGS-based methods reported, while also achieving state-of-the-art performance in terms of novel view synthesis compared to existing benchmarks.

Implications and Future Directions

The research implies a significant leap forward in the applicability of SLAM systems, particularly within the domain of autonomous navigation and outdoor robotics, where reliance on depth-sensing hardware poses practical limitations. The utilization of pure RGB data alleviates such constraints. From a theoretical standpoint, the proposed integration of pointmap regression with Gaussian splatting opens up new avenues for real-time, high-fidelity rendering in complex environments.

Subsequent research could explore the scalability of this approach, focusing on enhanced computational efficiency or robustness against dynamic environmental changes. Additionally, potential exists for extending the compatibility of this SLAM system with existing hardware implementations in industry sectors reliant on dependable navigation solutions without additional sensor overhead.

In conclusion, the paper presents a significant advancement in SLAM systems for outdoor environments, providing a constructive approach that elegantly circumvents the limitations posed by prior methodologies. The strategies laid out for accurate pose estimation and scene mapping using only RGB inputs could have wide-ranging impacts across various fields requiring spatial awareness and mapping capabilities.