NGM-SLAM: Gaussian Splatting SLAM with Radiance Field Submap
(2405.05702)Abstract
Gaussian Splatting has garnered widespread attention due to its exceptional performance. Consequently, SLAM systems based on Gaussian Splatting have emerged, leveraging its capabilities for rapid real-time rendering and high-fidelity mapping. However, current Gaussian Splatting SLAM systems usually struggle with large scene representation and lack effective loop closure adjustments and scene generalization capabilities. To address these issues, we introduce NGM-SLAM, the first GS-SLAM system that utilizes neural radiance field submaps for progressive scene expression, effectively integrating the strengths of neural radiance fields and 3D Gaussian Splatting. We have developed neural implicit submaps as supervision and achieve high-quality scene expression and online loop closure adjustments through Gaussian rendering of fused submaps. Our results on multiple real-world scenes and large-scale scene datasets demonstrate that our method can achieve accurate gap filling and high-quality scene expression, supporting both monocular, stereo, and RGB-D inputs, and achieving state-of-the-art scene reconstruction and tracking performance.
Overview
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NGM-SLAM integrates Neural Radiance Fields (NeRF) and Gaussian Splatting within a SLAM system to enhance scene representation, overcoming the speed and scalability issues typically faced in complex environments.
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The SLAM system addresses challenges like efficiently rendering large environments, accurately generalizing across different scenes, managing real-time loop closure adjustments, and synchronizing local and global mapping.
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NGM-SLAM supports various input types and showcases superior performance in environments that challenge traditional models, paving the way for advancements in robotics and immersive technology applications.
Understanding NGM-SLAM: Integrating Neural Radiance Fields with Gaussian Splatting for Enhanced Scene Representation
Introduction to NGM-SLAM
Simultaneous Localization and Mapping (SLAM) systems form the backbone of numerous applications spanning robotics and augmented/virtual reality. Traditional dense SLAM methods, though quite mature, often stagger at complex scene capturing due to limitations around high-fidelity modeling and real-time rendering constraints. Enter the neural implicit models, specifically those leveraging Neural Radiance Fields (NeRF), which have dramatically improved scene perception with environments richly textured and intricately detailed. However, speed and scalability remain hurdles. The paper discusses the integration of neural radiance fields and 3D Gaussian Splatting into a SLAM system named NGM-SLAM, aiming to marry the best of both worlds: enhanced scene representation from NeRF and speed from Gaussian Splatting.
Challenges Addressed by NGM-SLAM
NGM-SLAM primarily addresses four key challenges:
- Large Scene Representation: Traditional methods struggle with rendering large, complex environments efficiently.
- Accurate Scene Generalization: Most existing systems falter in generalizing across different scene types without specific prior data.
- Real-time Loop Closure Adjustments: Detecting and adjusting for loop closures in real-time is critical for maintaining accuracy in spatial understanding over time.
- Synchronization of Local and Global Mapping: Ensuring that local map updates correlate correctly with global scene understanding without extensive computational overhead.
Core Components of NGM-SLAM
Progressive Scene Building
- The system constructs the environment using "submaps," small, incrementally built maps that integrate over time to form a comprehensive global map.
- Each submap starts with keyframes and expands as more data becomes available, utilizing prior submaps to guide detailed rendering through Gaussian splatting.
Loop Closure and Global Adjustment
- A pivotal feature is the system's ability to detect loop closures and adjust mappings in real-time. This ensures that the environmental model remains consistent even as new information modifies the old understanding.
- NGM-SLAM implements a blend of local and global bundle adjustments to continuously refine the map fidelity and spatial accuracy.
Multi-Input Compatibility
- Uniquely, NGM-SLAM supports various input types including monocular, stereo, and RGB-D, making it adaptable to numerous hardware configurations and application requirements.
Performance Excellence
- The documented tests reveal state-of-the-art performance in scene understanding and tracking, particularly highlighting its prowess in environments typically challenging for other models, such as large-scale indoor scenes where detailed texturing and numerous occlusions occur.
Practical Implications and Future Directions
NGM-SLAM's ability to efficiently handle large-scale environments with high detail and real-time updating opens new avenues for robotics and AR/VR applications. In robotics, machines can navigate and interact with dynamic, complex environments more reliably. For AR/VR, the implications are even more profound, offering the potential for creating more immersive and interactive virtual worlds that closer mimic the richness of the real world.
Looking forward, the integration of NGM-SLAM's principles could lead to even more sophisticated systems that learn from a multitude of sensory inputs, possibly incorporating sound and tactile data to create multi-sensory mapping systems. Moreover, continued advancements in neural network efficiency and processing hardware could reduce the system's computational demands further, enabling its deployment in less powerful, consumer-grade technology.
Conclusion
NGM-SLAM represents a significant step forward in SLAM technology, adeptly addressing traditional shortcomings by harnessing the strengths of both Gaussian Splatting and Neural Radiance Fields. Its ability to provide detailed, real-time updates in large-scale environments promises to push the boundaries of what's possible in both robotics and immersive technology applications.
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