Abstract
Simultaneous Localization and Mapping (SLAM) with dense representation plays a key role in robotics, Virtual Reality (VR), and Augmented Reality (AR) applications. Recent advancements in dense representation SLAM have highlighted the potential of leveraging neural scene representation and 3D Gaussian representation for high-fidelity spatial representation. In this paper, we propose a novel dense representation SLAM approach with a fusion of Generalized Iterative Closest Point (G-ICP) and 3D Gaussian Splatting (3DGS). In contrast to existing methods, we utilize a single Gaussian map for both tracking and mapping, resulting in mutual benefits. Through the exchange of covariances between tracking and mapping processes with scale alignment techniques, we minimize redundant computations and achieve an efficient system. Additionally, we enhance tracking accuracy and mapping quality through our keyframe selection methods. Experimental results demonstrate the effectiveness of our approach, showing an incredibly fast speed up to 107 FPS (for the entire system) and superior quality of the reconstructed map.
Overview
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Introduces an innovative SLAM method combining G-ICP and 3DGS for efficient spatial mapping and localization, utilizing a singular Gaussian map.
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Achieves up to 107 FPS, significantly accelerating operational speed while improving spatial map quality.
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Evaluates performance using Replica and TUM datasets, demonstrating superior accuracy and speed in various environments.
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Outlines future enhancements, including integrating robust image features with depth information for improved resilience against sensor noise.
An Efficient SLAM Approach Through the Fusion of G-ICP and 3D Gaussian Splatting
Introduction
Simultaneous Localization and Mapping (SLAM) remains a cornerstone in the advancement of robotics and virtual/augmented reality technologies. This paper introduces an innovative dense representation SLAM method, integrating Generalized Iterative Closest Point (G-ICP) and 3D Gaussian Splatting (3DGS), which districts from prior practices by employing a singular Gaussian map for both tracking and mapping. This approach not only minimizes redundant computations but also significantly accelerates the system's operational speed, achieving up to 107 FPS, while concurrently improving the quality of the spatial map reconstructed.
Methodology
The introduced SLAM framework synergistically combines the strengths of G-ICP and 3DGS. It effectively uses covariances among tracking and mapping processes with scale alignment techniques, minimizing unnecessary computations and ensuring efficient system operation. Furthermore, the system employs dynamic keyframe selection, further enhancing tracking accuracy and map quality. Specifically, it operates by:
- Utilizing G-ICP for tracking by aligning the current frame with the 3DGS map, which simplifies the computation by directly leveraging the 3D information encoded in the covariances.
- Optimizing the scale and covariances of the 3D Gaussians in 3DGS for mapping, thereby ensuring the accuracy and quality of the 3D space representation.
- Implementing specialized techniques such as scale alignment to reinforce optimal performance between tracking and mapping.
Experimental Results
The system was rigorously evaluated using the Replica and TUM datasets, showcasing exemplary performance in both synthetic and real-world scenarios. On the Replica dataset, the proposed method demonstrated state-of-the-art accuracy in camera pose estimation, significantly outperforming existing methods and markedly reducing trajectory error. Regarding the TUM dataset, the method again proved competitive, showcasing its robustness even in challenging, noisy real-world environments. Notably, the system exhibited remarkable speed, with speeds up to 107 FPS, far surpassing current methods, without compromising on the quality of the reconstructed map.
Implications and Future Directions
The presented research advances the SLAM domain by providing a highly efficient method capable of real-time dense mapping and precise localization. By leveraging the mutual benefits of G-ICP and 3DGS, the method addresses the critical challenge of balancing computational efficiency with the fidelity of spatial representation. The experimental results affirm the approach's efficacy and underscore its potential applicability across various domains needing reliable and swift 3D environment mapping and navigation.
The paper also outlines a clear roadmap for future developments, hinting at the exploration of integrating robust image features alongside depth information to further enhance the system's resilience against sensor noise prevalent in real-world settings. This adaptation could potentially spearhead advancements in deploying SLAM in more dynamic and unstructured environments.
Conclusion
This research paper introduces an innovative SLAM framework that significantly enhances the speed and quality of 3D spatial mapping and localization through the integration of G-ICP and 3DGS. By sharing a single Gaussian map across tracking and mapping processes and applying scale alignment techniques, the method not only simplifies computations but also attains remarkable operational speed and map quality. The demonstrated superiority in both synthetic and real-world datasets underscores the method's potential to reshape the future trajectory of SLAM technology deployment.