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GigaSLAM: Large-Scale Monocular SLAM with Hierarchical Gaussian Splats (2503.08071v2)

Published 11 Mar 2025 in cs.RO and cs.CV

Abstract: Tracking and mapping in large-scale, unbounded outdoor environments using only monocular RGB input presents substantial challenges for existing SLAM systems. Traditional Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) SLAM methods are typically limited to small, bounded indoor settings. To overcome these challenges, we introduce GigaSLAM, the first RGB NeRF / 3DGS-based SLAM framework for kilometer-scale outdoor environments, as demonstrated on the KITTI, KITTI 360, 4 Seasons and A2D2 datasets. Our approach employs a hierarchical sparse voxel map representation, where Gaussians are decoded by neural networks at multiple levels of detail. This design enables efficient, scalable mapping and high-fidelity viewpoint rendering across expansive, unbounded scenes. For front-end tracking, GigaSLAM utilizes a metric depth model combined with epipolar geometry and PnP algorithms to accurately estimate poses, while incorporating a Bag-of-Words-based loop closure mechanism to maintain robust alignment over long trajectories. Consequently, GigaSLAM delivers high-precision tracking and visually faithful rendering on urban outdoor benchmarks, establishing a robust SLAM solution for large-scale, long-term scenarios, and significantly extending the applicability of Gaussian Splatting SLAM systems to unbounded outdoor environments. GitHub: https://github.com/DengKaiCQ/GigaSLAM.

Summary

Essay on "GigaSLAM: Large-Scale Monocular SLAM with Hierarchical Gaussian Splats"

The paper "GigaSLAM: Large-Scale Monocular SLAM with Hierarchical Gaussian Splats" introduces a novel framework designed to overcome the limitations faced by existing SLAM (Simultaneous Localization and Mapping) systems in large-scale, unbounded outdoor environments using monocular RGB inputs. This work expands the applicability of SLAM techniques, particularly those leveraging Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), which have been mostly constrained to small, bounded indoor environments. The authors propose GigaSLAM as a robust framework for kilometer-scale outdoor scenes, validated on challenging datasets such as KITTI and KITTI 360.

Core Contribution

The primary contribution of this research is the development of a hierarchical sparse voxel map representation. In this representation, Gaussian splats are decoded by neural networks at varying levels of detail, allowing for efficient mapping and high-fidelity viewpoint rendering across extensive scenes. The novel hierarchical approach facilitates scalable mapping by dynamically adjusting the resolution of the voxel grid according to the area's distance from the viewpoint, thus optimizing computational and memory resources.

Moreover, for pose estimation in large, outdoor sequences, GigaSLAM introduces a monocular metric depth model that operates in tandem with epipolar geometry and Perspective-n-Point (PnP) algorithms. The system also integrates a Bag-of-Words loop closure mechanism to effectively manage global alignment over long trajectories, addressing common drift issues found in large-scale SLAM implementations.

Experimental Evaluation

GigaSLAM was evaluated on urban outdoor sequences from the KITTI and KITTI 360 datasets, demonstrating robust performance in mapping and tracking. The experiments show that the framework outperforms traditional monocular SLAM methods, such as the well-regarded ORB-SLAM2, especially in maintaining tracking accuracy over long sequences. The proposed method's ability to handle the expansive nature of real-world outdoor environments establishes it as one of the pioneering contributions to the SLAM domain utilizing NeRF/3DGS in such challenging settings.

Theoretical and Practical Implications

The theoretical advancements inherent in GigaSLAM pave the way for further explorations into hierarchical scene representations and their application to expansive environments. The capacity to efficiently encode and render complex scenes at varying levels of detail opens new research avenues in object-level understanding and scene manipulation. From a practical perspective, GigaSLAM could significantly benefit applications in autonomous driving, drone navigation, and augmented reality where real-time performance in large-scale environments is crucial.

Future Directions

While GigaSLAM addresses significant challenges associated with large-scale outdoor mapping, further research could focus on improving robustness against environmental dynamics such as lighting changes or occlusions. Additionally, the impact of various sensor input methods, potentially integrating other modalities like LiDAR, could be an area of exploration to enhance depth accuracy and system robustness. An interesting development could involve fully integrating deep learning techniques within the tracking pipeline, further automating and potentially improving the adaptive refinement of the map representation.

In conclusion, "GigaSLAM: Large-Scale Monocular SLAM with Hierarchical Gaussian Splats" represents a substantial stride in the extension of SLAM methodologies to broader, more complex environments, with its hierarchical voxel-based framework pushing the boundaries of existing systems' operational scalability. This paper sets the foundation for ongoing research and application development within the field of computer vision and robotics.

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