S-Graphs 2.0 -- A Hierarchical-Semantic Optimization and Loop Closure for SLAM (2502.18044v2)

Abstract: The hierarchical structure of 3D scene graphs shows a high relevance for representations purposes, as it fits common patterns from man-made environments. But, additionally, the semantic and geometric information in such hierarchical representations could be leveraged to speed up the optimization and management of map elements and robot poses. In this direction, we present our work Situational Graphs 2.0 (S-Graphs 2.0), which leverages the hierarchical structure of indoor scenes for efficient data management and optimization. Our algorithm begins by constructing a situational graph that represents the environment into four layers: Keyframes, Walls, Rooms, and Floors. Our first novelty lies in the front-end, which includes a floor detection module capable of identifying stairways and assigning floor-level semantic relations to the underlying layers. Floor-level semantics allows us to propose a floor-based loop closure strategy, that effectively rejects false positive closures that typically appear due to aliasing between different floors of a building. Our second novelty lies in leveraging our representation hierarchy in the optimization. Our proposal consists of: (1) local optimization over a window of recent keyframes and their connected components across the four representation layers, (2) floor-level global optimization, which focuses only on keyframes and their connections within the current floor during loop closures, and (3) room-level local optimization, marginalizing redundant keyframes that share observations within the room, which reduces the computational footprint. We validate our algorithm extensively in different real multi-floor environments. Our approach shows state-of-art-art accuracy metrics in large-scale multi-floor environments, estimating hierarchical representations up to 10x faster, in average, than competing baselines

• The paper introduces S-Graphs 2.0, a hierarchical-semantic framework for optimizing SLAM in complex multi-floor indoor environments.
• It employs a four-layered hierarchical graph structure and multi-level optimization strategies for robust localization and mapping.
• Evaluated on real-world datasets, S-Graphs 2.0 improves accuracy and significantly reduces computational costs compared to baseline SLAM methods.

A Hierarchical Approach to SLAM Optimization with S-Graphs 2.0

The paper "S-Graphs 2.0 -- A Hierarchical-Semantic Optimization and Loop Closure for SLAM" introduces an advanced framework for improving Simultaneous Localization and Mapping (SLAM) in complex multi-floor indoor environments using hierarchical scene graph structures. This research presents a significant sophistication of SLAM methodologies, addressing major limitations in computational efficiency and map accuracy in large-scale scenarios.

Key Contributions

The authors propose the Situational Graphs 2.0 (S-Graphs 2.0) system, which integrates a set of robust strategies to enhance SLAM performance:

1. Hierarchical Graph Structure: The S-Graphs 2.0 system organizes environments into a four-layered computational graph encompassing Keyframes, Walls, Rooms, and Floors. This partitioning allows for semantic optimization and efficient tracking of the robot's observed environments, enhancing the robustness of localization tasks.
2. Improved Floor Segmentation and Detection: The introduction of a floor detection algorithm is a noteworthy innovation in S-Graphs 2.0. This module is adept at discerning building floors and staircases, ensuring that scene organization reflects semantic relations, and facilitating reliable loop closures. The distinction between visually similar areas across different floors effectively prevents erroneous map corrections.
3. Multi-level Optimization Strategy: The framework deploys hierarchical optimization mechanisms:
   • Local Optimization involves a focused optimization of recent keyframes, restricting the computational window to recently traversed areas.
   • Floor-Global Optimization operates selectively on identified floor levels during loop closures, mitigating unnecessary computational overhead.
   • Room-Local Optimization applies within enclosed spaces, reducing redundancy by marginalizing repeated observations.

Methodology and Evaluation

The paper provides extensive validation through real-world experiments in diverse environments, including university buildings and construction sites. The robustness of S-Graphs 2.0 is quantitatively supported by metrics such as the Root Mean Square Error (RMSE) and Mean Map Entropy (MME). On the single-floor datasets, the system matches or exceeds the superior state-of-the-art SLAM frameworks in terms of accuracy, while significantly reducing computational costs. On multi-floor datasets, it demonstrates superior segmentation capabilities, reflected in better metric scores than traditional methods.

Practical implications suggest that S-Graphs 2.0 can be instrumental in real-time robotic applications where computational resources are limited, and environmental mapping accuracies are essential. Moreover, this hierarchical approach can effortlessly adapt to complex indoor environments, which present challenges for many baseline SLAM systems not equipped to handle large-scale, multi-layered optimization tasks.

Future Directions

Future developments of the S-Graphs paradigm could involve deeper integration of semantic data from environmental contexts to refine further hierarchical optimizations and floor-level adjustments. Additionally, embedding machine learning-driven semantic understanding might improve robustness against dynamic or cluttered settings.

This paper presents a methodological enhancement in SLAM, blending hierarchical design with semantic optimization principles, offering an efficient yet effective solution to indoor robotic mapping and navigation challenges. The research implies a forward trajectory for SLAM research, where hierarchy and semantics play a pivotal role in advancing autonomy in robotic systems.