Loopy-SLAM: Dense Neural SLAM with Loop Closures (2402.09944v2)

Abstract: Neural RGBD SLAM techniques have shown promise in dense Simultaneous Localization And Mapping (SLAM), yet face challenges such as error accumulation during camera tracking resulting in distorted maps. In response, we introduce Loopy-SLAM that globally optimizes poses and the dense 3D model. We use frame-to-model tracking using a data-driven point-based submap generation method and trigger loop closures online by performing global place recognition. Robust pose graph optimization is used to rigidly align the local submaps. As our representation is point based, map corrections can be performed efficiently without the need to store the entire history of input frames used for mapping as typically required by methods employing a grid based mapping structure. Evaluation on the synthetic Replica and real-world TUM-RGBD and ScanNet datasets demonstrate competitive or superior performance in tracking, mapping, and rendering accuracy when compared to existing dense neural RGBD SLAM methods. Project page: notchla.github.io/Loopy-SLAM.

• The paper introduces a novel global optimization framework that integrates loop closures to mitigate error accumulation in dense neural SLAM.
• The paper presents an innovative dynamic submap generation and registration process that fuses online feature extraction with direct loop closure corrections.
• The paper demonstrates robust validation across synthetic and real-world datasets, significantly improving trajectory estimation and depth rendering accuracy.

Advancing Dense Neural SLAM with Loopy-SLAM: Integrating Loop Closure for Accurate and Efficient Mapping

Introduction to Loopy-SLAM

The field of online dense 3D reconstruction, particularly leveraging RGBD cameras, has been a focal point in the computational vision and robotic navigation fields. The newly introduced Loopy-SLAM approach attempts to further this progress by enhancing the neural RGBD SLAM techniques. While existing frameworks offer significant advancements, they often grapple with error accumulation during the camera tracking phase, resulting in distorted and inaccurate maps. Loopy-SLAM addresses these challenges by employing a novel global optimization framework for both poses and the generated dense 3D models, thereby promising to mitigate the aforementioned inaccuracies.

Core Contributions and Methodology

Loopy-SLAM introduces several innovative contributions to the field. Principally, it anchors neural features in point cloud submaps which grow iteratively, thereby offering a dynamic and efficient approach to scene mapping. This dynamic creation of submaps in response to camera motion, alongside progressive construction of a pose graph, underpins Loopy-SLAM's ability to dynamically and accurately represent the scanned environment. Crucially, the methodology includes an online global place recognition feature that facilitates loop closures, allowing for the global alignment of trajectory and submaps efficiently.

Further innovation is observed in the approach to loop closure. Unlike traditional methods that necessitate gradient updates of the scene representation or complex reintegration strategies, Loopy-SLAM employs a direct implementation method. This enables swift and straightforward map corrections without compromising the system's efficiency or the fidelity of the map. Another noteworthy aspect of Loopy-SLAM is its refined submap registration method, designed to circumvent visible seams at overlapping regions— a common issue in dense mapping. By refining features and performing feature fusion at the trajectory's conclusion, Loopy-SLAM ensures a seamless and coherent 3D scene representation.

Empirical Validation

Loopy-SLAM's efficacy is demonstrated through comprehensive evaluations conducted across various datasets, including the synthetic Replica as well as the real-world TUM-RGBD and ScanNet datasets. These experiments compare Loopy-SLAM against leading dense neural SLAM frameworks like Point-SLAM and ESLAM, showcasing competitive or superior performance in terms of tracking, mapping, and rendering accuracy. For instance, on the Replica dataset, Loopy-SLAM achieved significant improvements in Absolute Trajectory Error (ATE) and depth re-rendering error metrics. Similarly convincing results were observed on TUM-RGBD and ScanNet datasets, reinforcing Loopy-SLAM's robustness and the high fidelity of its generated dense maps.

Future Directions and Challenges

The introduction of Loopy-SLAM marks a significant stride towards resolving long-standing issues in the field of dense neural SLAM. However, several avenues for future exploration and enhancement emerge. The integration of more sophisticated tracking mechanisms, robust global place recognition, and real-time optimization strategies could further refine accuracy and efficiency. Additionally, addressing scalability concerns for extensive scene reconstruction, optimizing computational resources, and ensuring real-time operability present critical challenges for broader application and adoption.

Conclusion

Loopy-SLAM sets a new benchmark in the dense neural SLAM domain by adeptly combining neural scene representation with online loop closures. The methodology's successful mitigation of error accumulation and efficient map corrections heralds a leap towards more accurate and dynamically optimized 3D scene reconstructions. As this research progresses, the continuous evolution of Loopy-SLAM could significantly impact various applications, from autonomous navigation to augmented reality, rendering it an essential tool in the advancement of computer vision and robotics.