S-Graphs+: Real-time Localization and Mapping leveraging Hierarchical Representations (2212.11770v3)

Abstract: In this paper, we present an evolved version of Situational Graphs, which jointly models in a single optimizable factor graph (1) a pose graph, as a set of robot keyframes comprising associated measurements and robot poses, and (2) a 3D scene graph, as a high-level representation of the environment that encodes its different geometric elements with semantic attributes and the relational information between them. Specifically, our S-Graphs+ is a novel four-layered factor graph that includes: (1) a keyframes layer with robot pose estimates, (2) a walls layer representing wall surfaces, (3) a rooms layer encompassing sets of wall planes, and (4) a floors layer gathering the rooms within a given floor level. The above graph is optimized in real-time to obtain a robust and accurate estimate of the robots pose and its map, simultaneously constructing and leveraging high-level information of the environment. To extract this high-level information, we present novel room and floor segmentation algorithms utilizing the mapped wall planes and free-space clusters. We tested S-Graphs+ on multiple datasets, including simulated and real data of indoor environments from varying construction sites, and on a real public dataset of several indoor office areas. On average over our datasets, S-Graphs+ outperforms the accuracy of the second-best method by a margin of 10.67%, while extending the robot situational awareness by a richer scene model. Moreover, we make the software available as a docker file.

• The paper introduces a real-time SLAM framework that leverages a four-layered hierarchical factor graph to integrate LiDAR data with 3D scene graphs.
• Advanced room and floor segmentation algorithms enable a 10.67% accuracy improvement over other state-of-the-art SLAM methods.
• The method enhances situational awareness in indoor environments, offering practical benefits for autonomous navigation and robotics applications.

Insights into S-Graphs+: Real-time Localization and Mapping leveraging Hierarchical Representations

The paper "S-Graphs+: Real-time Localization and Mapping leveraging Hierarchical Representations" introduces a novel approach to simultaneous localization and mapping (SLAM) by coupling geometric LiDAR SLAM with 3D scene graphs through a four-layered factor graph framework. The authors propose an advanced version of the Situational Graphs (S-Graphs), which addresses previous limitations by optimizing the graph in real-time, thereby achieving superior accuracy and robustness in robot localization and mapping.

Composition of S-Graphs+

S-Graphs+ integrates multiple layers:

1. Keyframes Layer: This consists of robot pose estimates derived at specific time intervals.
2. Walls Layer: This represents wall surfaces and is comprised of plane parameters.
3. Rooms Layer: This includes sets of wall planes that define various rooms.
4. Floors Layer: This aggregates rooms within a specific floor level.

The hierarchical representation allows these layers to be continuously optimized, generating a robust estimate of both the robot's trajectory and the surrounding map. The paper introduces sophisticated algorithms for room and floor segmentation, enhancing understanding and situational awareness of the robot in complex environments.

Numerical Results and Comparisons

The paper reports that S-Graphs+ surpasses other SLAM methodologies in accuracy. Specifically, it achieves an average improvement of 10.67% over the second-best SLAM method across various datasets. These datasets span both simulated and real-world environments, demonstrating the method's applicability to diverse indoor scenarios. The significant accuracy gains are attributed to the integrated high-level scene understanding, leveraging hierarchical data effectively.

Experimental Evaluation

The comprehensive experiments include a comparison with state-of-the-art SLAM algorithms such as ALOAM, HDL-SLAM, MLOAM, FLOAM, and LeGO-LOAM. On large datasets, S-Graphs+ demonstrates substantial reductions in error, exhibiting robustness in scenarios with complex and cluttered environments.

Theoretical Implications

The hierarchical factor graph represents a powerful framework that can potentially transform how SLAM is approached by integrating semantic and metric information in real-time. This allows the system to maintain a detailed and semantically-enriched model of the environment, benefitting robotics applications requiring high-level understanding.

Practical Implications and Future Directions

For practical applications, especially in construction and office environments, this technology holds promise for improving autonomous navigation and operational efficiency. Future developments using this framework might explore further optimization techniques for faster processing, consider scaling to larger environments with multiple floors, and enhance scene understanding beyond current capabilities.

In conclusion, the paper's contribution to the field of SLAM through the integration of hierarchical models illustrates a significant step forward. By emphasizing continuous optimization and real-time performance, S-Graphs+ presents a compelling alternative to existing SLAM methodologies in robotics, offering enhanced accuracy and scene comprehension. As the framework evolves, it promises to pave the way for more sophisticated autonomous systems capable of navigating and interacting with highly dynamic and structured environments.