Hier-SLAM++: Neuro-Symbolic Semantic SLAM with a Hierarchically Categorical Gaussian Splatting (2502.14931v1)

Abstract: We propose Hier-SLAM++, a comprehensive Neuro-Symbolic semantic 3D Gaussian Splatting SLAM method with both RGB-D and monocular input featuring an advanced hierarchical categorical representation, which enables accurate pose estimation as well as global 3D semantic mapping. The parameter usage in semantic SLAM systems increases significantly with the growing complexity of the environment, making scene understanding particularly challenging and costly. To address this problem, we introduce a novel and general hierarchical representation that encodes both semantic and geometric information in a compact form into 3D Gaussian Splatting, leveraging the capabilities of LLMs as well as the 3D generative model. By utilizing the proposed hierarchical tree structure, semantic information is symbolically represented and learned in an end-to-end manner. We further introduce a novel semantic loss designed to optimize hierarchical semantic information through both inter-level and cross-level optimization. Additionally, we propose an improved SLAM system to support both RGB-D and monocular inputs using a feed-forward model. To the best of our knowledge, this is the first semantic monocular Gaussian Splatting SLAM system, significantly reducing sensor requirements for 3D semantic understanding and broadening the applicability of semantic Gaussian SLAM system. We conduct experiments on both synthetic and real-world datasets, demonstrating superior or on-par performance with state-of-the-art NeRF-based and Gaussian-based SLAM systems, while significantly reducing storage and training time requirements.

Hier-SLAM++: Neuro-Symbolic Semantic SLAM with Hierarchically Categorical Gaussian Splatting

The research paper presents "Hier-SLAM++," a novel approach in the field of Simultaneous Localization and Mapping (SLAM), leveraging neuro-symbolic methods and hierarchical categorization to enhance semantic mapping in complex environments. This method integrates semantic understanding into 3D Gaussian Splatting-based SLAM systems, an area that has traditionally focused on geometric reconstruction, offering a comprehensive solution that addresses both scene perception and pose estimation using monocular and RGB-D sensor inputs.

Methodology Overview

Hier-SLAM++ introduces a unique hierarchical representation for semantic information, which efficiently compresses and encodes both semantic and geometric properties. This hierarchical tree structure is generated by utilizing LLMs for semantic knowledge and 3D generative models for geometric shape details. This blend of technologies enables the encoding of scene semantics in a compact form, significantly enhancing its computational and storage efficiency.

The system employs a hierarchical tree structure where semantic classes are symbolically represented through hierarchical paths from root to leaf. This structure not only captures the semantic essence but also facilitates efficient parameter optimization across multiple levels of detail. The use of hierarchical semantic loss, featuring inter-level and cross-level optimization, ensures a robust semantic understanding that scales well with the complexity of the environment.

One of the standout features of Hier-SLAM++ is its support for monocular inputs. By incorporating a 3D feed-forward model, it provides geometric priors that eliminate the need for depth sensors, thus broadening its applicability in various practical scenarios.

Experimental Results

The efficacy of Hier-SLAM++ is demonstrated through empirical evaluations on both synthetic (Replica) and real-world (ScanNet, TUM-RGBD) datasets. Hier-SLAM++ achieves superior or comparable performance to leading NeRF-based and Gaussian-based SLAM systems, as evidenced by metrics like ATE RMSE for localization, Depth L1 for mapping, PSNR, SSIM, LPIPS for rendering quality, and mIoU for semantic understanding.

In particular, the system evidences strong scalability and robustness across diverse environments with varying complexities. Its efficient semantic coding and reduced training requirements mean it performs well even with slower computational rates, thereby providing a pragmatic solution for time-sensitive applications in robotics and augmented reality.

Implications and Future Directions

Hier-SLAM++ substantially pushes the boundaries by marrying semantic and geometric mapping capabilities within SLAM, suggesting potential pathways for more intelligent and autonomous systems that comprehend and interact with their surroundings using less heavily instrumented setups. This could pave the way for advancements in autonomous navigation, robotic manipulation, and mixed reality interfaces.

Future work could explore further optimization of hierarchical representations and loss functions, possibly integrating more sophisticated machine learning techniques to enhance robustness and accuracy. Additionally, exploring the impact of various hierarchical tree structures and their scalability concerning more extensive real-world datasets could yield deeper insights into optimizing neuro-symbolic SLAM systems.

Hier-SLAM++ opens intriguing avenues in semantic SLAM, promising enhanced real-time performance and greater applicability, thereby offering significant contributions to the fields of machine perception and intelligent systems.