HS-SLAM: Hybrid Representation with Structural Supervision for Improved Dense SLAM (2503.21778v1)

Abstract: NeRF-based SLAM has recently achieved promising results in tracking and reconstruction. However, existing methods face challenges in providing sufficient scene representation, capturing structural information, and maintaining global consistency in scenes emerging significant movement or being forgotten. To this end, we present HS-SLAM to tackle these problems. To enhance scene representation capacity, we propose a hybrid encoding network that combines the complementary strengths of hash-grid, tri-planes, and one-blob, improving the completeness and smoothness of reconstruction. Additionally, we introduce structural supervision by sampling patches of non-local pixels rather than individual rays to better capture the scene structure. To ensure global consistency, we implement an active global bundle adjustment (BA) to eliminate camera drifts and mitigate accumulative errors. Experimental results demonstrate that HS-SLAM outperforms the baselines in tracking and reconstruction accuracy while maintaining the efficiency required for robotics.

An In-Depth Examination of HS-SLAM: Hybrid Representation with Structural Supervision for Improved Dense SLAM

In recent developments within the field of simultaneous localization and mapping (SLAM), the paper "HS-SLAM: Hybrid Representation with Structural Supervision for Improved Dense SLAM" puts forth an innovative framework tailored to enhance scene representation and structural comprehension in dense SLAM applications. This paper addresses prevalent challenges faced by existing SLAM paradigms, particularly those grounded in Neural Radiance Fields (NeRF), which typically falter in preserving structural integrity, achieving global consistency, and adequately representing intricate scene details during substantial movements or potential data forgetfulness. This essay delineates the distinctive contributions of HS-SLAM, contextualizes its performance relative to established methods, and speculates on its implications for future advancements in robotics and AI-driven navigation systems.

Core Contributions and Methodology

The HS-SLAM framework is composed of three pivotal elements: hybrid scene representation, structural supervision through patch sampling, and active global bundle adjustment (BA). Each component is thoughtfully integrated to surmount the constraints encountered in traditional NeRF-based and 3D Gaussian Splatting (GS) SLAM systems.

1. Hybrid Scene Representation: Unlike traditional SLAM systems that rely predominantly on singular forms of spatial encoding, HS-SLAM leverages a hybrid approach combining hash grids, tri-planes, and one-blob encodings. This fusion harnesses the advantages of capturing high-frequency scene details (via hash grids) and low-frequency information, such as coherence and global structure, through tri-planes and one-blob encodings, respectively. This hybrid strategy ensures a more comprehensive scene representation, bolstering both the accuracy and smoothness of reconstructions.
2. Structural Supervision: A novel aspect of HS-SLAM lies in its utilization of structural supervision, employing a patch-based technique as opposed to conventional ray-based sampling. By exploiting the Structural Similarity Index Measure (SSIM), HS-SLAM samples patches of non-local pixels to capture cohesive structural cues across the scene. This methodology brings forth a more nuanced understanding of the scene's spatial arrangements, surpassing the capabilities of prior systems focused purely on pixel-level fidelity.
3. Active Global Bundle Adjustment: HS-SLAM introduces a dynamic approach to global BA, emphasizing active sampling from keyframes exhibiting significant discrepancies in scene rendering. This strategy adeptly curtails camera drifts and accumulative errors by prioritizing frames with suboptimal trajectory poses, effectively enhancing tracking precision and maintaining scene consistency.

Empirical Evaluation

The empirical assessment of HS-SLAM across datasets such as Replica, ScanNet, and TUM RGB-D showcases its superior performance in both mapping and tracking tasks. Compared to competitive NeRF-centric frameworks like Co-SLAM and PLGSLAM, HS-SLAM demonstrates marked improvements in depth accuracy, completion rate, and Absolute Trajectory Error (ATE). These results affirm the efficacy of the hybrid encoding and structural supervision in mitigating the artefacts and errors that often hinder SLAM applications.

Particularly noteworthy is HS-SLAM's performance on the Replica dataset, where it achieves significant strides in reconstruction accuracy with high fidelity in rendered scenes. This advancement is attributed to the effective integration of complementary encoding techniques and the introduction of active global BA, which collectively elevate both precision and computational efficiency.

Implications and Future Directions

HS-SLAM stands as a testament to the potential of hybrid encoding frameworks in revolutionizing dense SLAM systems, especially in resource-constrained robotics environments. By circumventing the limitations of single-encoding approaches and harnessing structural supervision, HS-SLAM sets a precedent for future SLAM research to explore multi-faceted scene representations and dynamic adjustment strategies.

Looking forward, enhancements in scalability and real-time processing present fertile grounds for exploration. Future work may center around extending the applicability of HS-SLAM in outdoor or larger-scale environments, integrating advanced loop closure techniques, or optimizing algorithmic efficiency to facilitate deployment in autonomous navigation systems.

In summary, HS-SLAM embodies a significant step forward in dense SLAM technology, offering a balanced convergence of accuracy, structural comprehension, and operational efficiency. As such, it provides a promising foundation for ongoing research and development in autonomous systems and AI-driven spatial intelligence.