NeSLAM: Neural Implicit Mapping and Self-Supervised Feature Tracking With Depth Completion and Denoising
(2403.20034)Abstract
In recent years, there have been significant advancements in 3D reconstruction and dense RGB-D SLAM systems. One notable development is the application of Neural Radiance Fields (NeRF) in these systems, which utilizes implicit neural representation to encode 3D scenes. This extension of NeRF to SLAM has shown promising results. However, the depth images obtained from consumer-grade RGB-D sensors are often sparse and noisy, which poses significant challenges for 3D reconstruction and affects the accuracy of the representation of the scene geometry. Moreover, the original hierarchical feature grid with occupancy value is inaccurate for scene geometry representation. Furthermore, the existing methods select random pixels for camera tracking, which leads to inaccurate localization and is not robust in real-world indoor environments. To this end, we present NeSLAM, an advanced framework that achieves accurate and dense depth estimation, robust camera tracking, and realistic synthesis of novel views. First, a depth completion and denoising network is designed to provide dense geometry prior and guide the neural implicit representation optimization. Second, the occupancy scene representation is replaced with Signed Distance Field (SDF) hierarchical scene representation for high-quality reconstruction and view synthesis. Furthermore, we also propose a NeRF-based self-supervised feature tracking algorithm for robust real-time tracking. Experiments on various indoor datasets demonstrate the effectiveness and accuracy of the system in reconstruction, tracking quality, and novel view synthesis.
Overview
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NeSLAM integrates a depth completion and denoising network, Signed Distance Field (SDF) based hierarchical scene representation, and a NeRF-based self-supervised feature tracking algorithm to enhance dense RGB-D SLAM systems.
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The depth completion and denoising network improves the quality of sparse and noisy depth images from RGB-D sensors, facilitating better 3D reconstructions.
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A novel self-supervised feature tracking algorithm ensures robust and real-time tracking in complex indoor scenes, enabling precise camera localization.
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Extensive experiments demonstrate NeSLAM's superiority in reconstruction accuracy, tracking quality, and novel view synthesis compared to existing methods.
NeSLAM: Enhancing Dense RGB-D SLAM with Neural Implicit Mapping and Self-Supervised Feature Tracking
Introduction
Simultaneous Localization and Mapping (SLAM) systems are paramount in the field of robotics and virtual reality, enabling devices to understand and navigate through complex environments. The advent of Neural Radiance Fields (NeRF) has opened new avenues for achieving more detailed and accurate 3D reconstructions by leveraging implicit neural representations. Despite the promising results, challenges persist, particularly with depth images from consumer-grade RGB-D sensors that are often sparse, noisy, and thus detrimental to the fidelity of 3D reconstructions. Furthermore, traditional methods of feature tracking lack the robustness needed for accurate localization in varied environments. Addressing these challenges, NeSLAM introduces a sophisticated framework that integrates a depth completion and denoising network, Signed Distance Field (SDF) based hierarchical scene representation, and a NeRF-based self-supervised feature tracking algorithm, significantly advancing the capabilities of dense RGB-D SLAM systems.
Neural Implicit Mapping
NeSLAM proposes an advanced neural implicit mapping framework that leverages a novel depth completion and denoising network to process sparse and noisy depth images acquired from standard RGB-D sensors. This network significantly improves geometry prior for guiding the neural implicit representation optimization. By replacing the traditional occupancy scene representation with an SDF hierarchical scene representation, NeSLAM achieves higher quality reconstruction and view synthesis, which are crucial for robust SLAM operations. The use of SDF values over occupancy values enables a more accurate representation of scene geometry, enhancing the system's ability to model complex environments accurately.
Self-Supervised Feature Tracking
A key contribution of NeSLAM is its NeRF-based self-supervised feature tracking algorithm that ensures robust and real-time tracking in large and complex indoor scenes. By incorporating a self-supervised optimization technique that refines feature tracking during operation, the system exhibits superior generalization capabilities across diverse environments. This feature tracking method enables precise camera localization, a critical component for effective SLAM.
Experimental Results
Extensive experiments across various indoor datasets underscore the effectiveness of NeSLAM in reconstruction accuracy, tracking quality, and novel view synthesis. The introduction of a depth completion and denoising network alongside an SDF-based hierarchical scene representation allows for remarkable improvements in capturing detailed scene geometry and generating photo-realistic novel views. The NeRF-based self-supervised feature tracking further enhances localization accuracy and system robustness, surpassing the performance of existing and concurrent methods that employ implicit mapping approaches.
Implications and Future Directions
NeSLAM presents a significant advancement in dense RGB-D SLAM by addressing critical challenges associated with depth image sparsity, noise, and robust feature tracking. The integration of neural implicit techniques with traditional SLAM frameworks holds promise for developing more accurate, robust, and versatile systems capable of detailed 3D reconstruction and precise localization in dynamic and complex environments. Future research may explore the extension of this work to outdoor environments, dynamic scene understanding, and applications in autonomous navigation and augmented reality.
Conclusion
NeSLAM introduces an innovative approach to enhance dense RGB-D SLAM systems through neural implicit mapping and self-supervised feature tracking. Its ability to produce highly accurate 3D reconstructions, robust camera localization, and realistic novel view synthesis represents a notable contribution to the field. This research paves the way for future advancements in SLAM technology, with potential applications spanning robotics, virtual/augmented reality, and beyond.