SemGauss-SLAM: Dense Semantic Gaussian Splatting SLAM (2403.07494v3)

Abstract: We propose SemGauss-SLAM, a dense semantic SLAM system utilizing 3D Gaussian representation, that enables accurate 3D semantic mapping, robust camera tracking, and high-quality rendering simultaneously. In this system, we incorporate semantic feature embedding into 3D Gaussian representation, which effectively encodes semantic information within the spatial layout of the environment for precise semantic scene representation. Furthermore, we propose feature-level loss for updating 3D Gaussian representation, enabling higher-level guidance for 3D Gaussian optimization. In addition, to reduce cumulative drift in tracking and improve semantic reconstruction accuracy, we introduce semantic-informed bundle adjustment leveraging multi-frame semantic associations for joint optimization of 3D Gaussian representation and camera poses, leading to low-drift tracking and accurate mapping. Our SemGauss-SLAM method demonstrates superior performance over existing radiance field-based SLAM methods in terms of mapping and tracking accuracy on Replica and ScanNet datasets, while also showing excellent capabilities in high-precision semantic segmentation and dense semantic mapping.

• The paper introduces a novel dense semantic SLAM method by embedding semantic features within 3D Gaussian representations.
• It employs feature-level loss to accelerate optimization, resulting in improved convergence and mapping precision.
• The paper implements semantic-informed bundle adjustment to reduce cumulative drift and enhance real-time tracking performance.

SemGauss-SLAM: Advancing Dense Semantic Mapping with 3D Gaussian Representation

Introduction to SemGauss-SLAM

SemGauss-SLAM introduces a pioneering approach to dense semantic Simultaneous Localization and Mapping (SLAM) by incorporating 3D Gaussian representation. This novel strategy advances the capabilities of SLAM systems in achieving accurate 3D semantic mapping, robust camera tracking, and high-quality rendering in real-time. Traditional semantic SLAM systems have struggled with predicting unknown areas and requiring considerable map storage, while the recent advancements leveraging Neural Radiance Fields (NeRF) have faced challenges with inefficient rendering. SemGauss-SLAM addresses these limitations by effectively embedding semantic feature information within 3D Gaussian representations, introducing feature-level loss for efficient 3D Gaussian optimization, and implementing semantic-informed bundle adjustment to enhance reconstruction accuracy and reduce cumulative drift.

Key Contributions

• Semantic Gaussian Representation: SemGauss-SLAM pioneers in utilizing 3D Gaussians augmented with semantic feature embedding for dense semantic mapping, enabling precise construction of semantic maps and facilitating efficient 3D scene optimization.
• Feature-Level Loss: For optimizing the 3D Gaussian representation, feature-level loss offers a higher-level guidance that accelerates the convergence of semantic scene representation.
• Semantic-Informed Bundle Adjustment: By introducing a bundle adjustment process that leverages semantic associations for optimization, SemGauss-SLAM significantly reduces cumulative drift and enhances the accuracy of both mapping and tracking.

Superior Performance

SemGauss-SLAM demonstrates distinguished performance over existing dense semantic SLAM methods, particularly in mapping and tracking accuracy across challenging datasets like Replica and ScanNet. It not only excels in the precision of semantic segmentation and novel-view synthesis but also showcases remarkable abilities in 3D semantic mapping. The method's congruence in advancing state-of-the-art SLAM capabilities is substantiated by extensive quantitative evaluations showcasing its prowess in rendering quality, reconstruction accuracy, semantic segmentation precision, and tracking robustness.

Theoretical and Practical Implications

The introduction and implementation of SemGauss-SLAM have profound implications both theoretically and practically. Theoretically, the approach enriches literature on dense semantic SLAM by showcasing the feasibility and effectiveness of semantic feature embedding in 3D Gaussian representation. It elucidates the potential of feature-level loss in streamlining the convergence of optimization processes for complex scene representations. Practically, SemGauss-SLAM offers a realizable path towards enhancing autonomous systems' understandings, such as in robotics and autonomous vehicles, by enabling them to perform real-time, accurate semantic mapping and robust tracking in dynamically changing environments.

Speculations on Future Developments

Looking ahead, the principles and methodologies underpinning SemGauss-SLAM could inspire further explorations into the integration of semantic understanding within spatial mapping in various domains. Future research may delve into addressing the challenges of scalability and computational efficiency in deploying SemGauss-SLAM within large-scale environments. Moreover, expanding SemGauss-SLAM to incorporate dynamic object tracking and interaction within semantic mappings could open new avenues for interactive robotics and augmented reality applications. The foundational work of SemGauss-SLAM thus paves the way for a future where dense semantic SLAM technologies elevate machines' perception and interaction capabilities within complex spatial and semantic contexts.