Hydra-Multi: Collaborative Online Construction of 3D Scene Graphs with Multi-Robot Teams (2304.13487v1)

Abstract: 3D scene graphs have recently emerged as an expressive high-level map representation that describes a 3D environment as a layered graph where nodes represent spatial concepts at multiple levels of abstraction (e.g., objects, rooms, buildings) and edges represent relations between concepts (e.g., inclusion, adjacency). This paper describes Hydra-Multi, the first multi-robot spatial perception system capable of constructing a multi-robot 3D scene graph online from sensor data collected by robots in a team. In particular, we develop a centralized system capable of constructing a joint 3D scene graph by taking incremental inputs from multiple robots, effectively finding the relative transforms between the robots' frames, and incorporating loop closure detections to correctly reconcile the scene graph nodes from different robots. We evaluate Hydra-Multi on simulated and real scenarios and show it is able to reconstruct accurate 3D scene graphs online. We also demonstrate Hydra-Multi's capability of supporting heterogeneous teams by fusing different map representations built by robots with different sensor suites.

• The paper introduces Hydra-Multi, a system that enables real-time, collaborative construction of semantically-rich 3D scene graphs.
• It presents a novel align-optimize-reconcile framework with hierarchical loop closure detection to improve multi-robot mapping accuracy.
• Experiments demonstrate high localization precision and enhanced map quality, confirming Hydra-Multi’s practical effectiveness in diverse scenarios.

Essay: Collaborative Online Construction of 3D Scene Graphs with Multi-Robot Teams

The paper, titled "Hydra-Multi: Collaborative Online Construction of 3D Scene Graphs with Multi-Robot Teams," presents a seminal system known as Hydra-Multi, geared towards enhancing spatial perception through real-time construction of 3D scene graphs by teams of autonomous robots. The core innovation of Hydra-Multi is its ability to leverage multi-robot collaboration to build a layered, semantically-rich representation of complex environments, facilitating superior situational awareness and decision-making capabilities for robotic teams.

Technical Contributions

Hydra-Multi is structured into two main components: a frontend and a backend. The frontend interfaces the centralized control station with individual robots, handling scene graph inputs and facilitating loop closure detection. The backend is responsible for optimizing the scene graph to achieve a unified representation. Key features of the system include hierarchical loop closure detection, robust initial frame alignment using inter-robot loop closures, and an align-optimize-reconcile framework utilizing Graduated Non-Convexity (GNC) for optimizing multi-robot 3D scene graphs.

Results and Performance Metrics

The paper evaluates Hydra-Multi across simulated and real-world scenarios, demonstrating its proficiency in constructing centralized 3D scene graphs. In particular, Hydra-Multi exhibits:

• Comparable accuracy to state-of-the-art vision-based and LIDAR-based SLAM systems, with slight improvements over certain baselines.
• Effective reconciliation of heterogeneous map representations from robots with diverse sensors or mapping pipelines.
• Robust performance in environments with perceptual aliasing and different initial conditions for robot teams.

Experiments reveal Hydra-Multi's efficacy in mapping environments with high localization accuracy (e.g., Average Trajectory Error results emphasizing precision) while significantly improving map quality through collaboration. Objects, places, and room detection metrics assure the reliability of scene graph abstractions produced by Hydra-Multi vis-à-vis ground-truth data.

Implications and Future Directions

The implications of Hydra-Multi for robotics are substantial, providing a platform for efficient exploration and mapping by robotic teams in large-scale environments. The ability to construct real-time, hierarchical scene graphs offers potential advancements in applications such as autonomous navigation, environmental monitoring, and intelligent transportation systems.

The paper suggests several future avenues to extend the capabilities of Hydra-Multi. Transitioning from centralized to distributed processing can provide scalability for large deployments, while reducing dependency on bandwidth usage. Additionally, handling dynamic changes in environments and effectively integrating with existing infrastructure could amplify Hydra-Multi's utility further.

Conclusion

Hydra-Multi demonstrates a significant advancement in collaborative spatial perception, enabling multi-robot teams to effectively explore and represent complex environments. The robust implementation and extensive evaluation underline its practical applicability and pave the way for further developments in collaborative autonomous systems. The research presents strong foundational work conducive to ongoing explorations in enhancing semantic mapping capabilities and optimizing the synergy of multi-robot operations.