Foundations of Spatial Perception for Robotics: Hierarchical Representations and Real-time Systems (2305.07154v1)

Abstract: 3D spatial perception is the problem of building and maintaining an actionable and persistent representation of the environment in real-time using sensor data and prior knowledge. Despite the fast-paced progress in robot perception, most existing methods either build purely geometric maps (as in traditional SLAM) or flat metric-semantic maps that do not scale to large environments or large dictionaries of semantic labels. The first part of this paper is concerned with representations: we show that scalable representations for spatial perception need to be hierarchical in nature. Hierarchical representations are efficient to store, and lead to layered graphs with small treewidth, which enable provably efficient inference. We then introduce an example of hierarchical representation for indoor environments, namely a 3D scene graph, and discuss its structure and properties. The second part of the paper focuses on algorithms to incrementally construct a 3D scene graph as the robot explores the environment. Our algorithms combine 3D geometry, topology (to cluster the places into rooms), and geometric deep learning (e.g., to classify the type of rooms the robot is moving across). The third part of the paper focuses on algorithms to maintain and correct 3D scene graphs during long-term operation. We propose hierarchical descriptors for loop closure detection and describe how to correct a scene graph in response to loop closures, by solving a 3D scene graph optimization problem. We conclude the paper by combining the proposed perception algorithms into Hydra, a real-time spatial perception system that builds a 3D scene graph from visual-inertial data in real-time. We showcase Hydra's performance in photo-realistic simulations and real data collected by a Clearpath Jackal robots and a Unitree A1 robot. We release an open-source implementation of Hydra at https://github.com/MIT-SPARK/Hydra.

• The paper presents a framework that integrates hierarchical 3D scene graphs to reduce computational complexity and optimize memory usage.
• The paper develops Hydra, a system that incrementally constructs scene graphs in real time using techniques from geometric deep learning and topology.
• The paper achieves robust loop closure detection and consistent updates across multiple abstraction layers, enhancing navigation in complex environments.

Foundations of Spatial Perception for Robotics: Hierarchical Representations and Real-time Systems

The paper focuses on designing an advanced system for 3D spatial perception in robotics, emphasizing the need for hierarchical representations to efficiently model large and complex environments. The authors propose a framework that integrates multiple levels of abstraction in scene understanding using 3D scene graphs to seamlessly bridge geometric and semantic data. Hydra, the real-time system developed, exemplifies this integration, offering a solution that processes information hierarchically and incrementally.

Key Contributions

1. Hierarchical Representations

The paper argues that hierarchical representations are necessary for scalable and efficient 3D spatial perception. Unlike flat metric-semantic maps, hierarchical models can handle large dictionaries of semantic labels and vast environmental data more effectively. They demonstrate numerically that this approach minimizes memory usage and reduces computational complexity, enabling efficient inference through lower treewidth hierarchical graphs.

2. Real-time Incremental Construction

The system incrementally constructs a 3D scene graph from sensor data, enabling real-time operation. The authors leverage techniques from geometric deep learning and topology to integrate spatial concepts at various abstraction levels—from basic geometry to complex semantic understanding of environments, such as rooms and objects.

3. Persistent Representation and Loop Closure

The research develops novel hierarchical descriptors for loop closure detection, enhancing reliability in recognizing previously visited locations. The integration of a 3D scene graph optimization framework ensures that any detected loop closures result in accurate and consistent updates across various layers of the scene graph.

Evaluation and Performance

Hydra is evaluated in both simulated and real environments. The results indicate its ability to process scenes accurately in real time, with improvements in detecting accurate loop closures compared to traditional methods. The system's performance remains consistent across diverse setups, underscoring the robustness of hierarchical organization in spatial perception tasks.

Practical and Theoretical Implications

The proposed approaches have significant implications both practically and theoretically. Practically, they offer a scalable and versatile solution for perception tasks in complex environments, essential for autonomous robots navigating dynamic settings. Theoretically, the discussion of treewidth and hierarchical graphs presents new avenues for efficient inference in probabilistic models.

Future work could extend Hydra to more extensive navigation datasets, potentially refining semantic classification and expanding the hierarchical framework to larger environments. The integration of neural models for various sub-symbolic components could also enhance object and scene representation.

In summary, this paper provides a comprehensive computational framework for real-time 3D spatial perception using hierarchical representations, advancing the capabilities of robotic systems in understanding complex environments.