3-D Scene Graph: A Sparse and Semantic Representation of Physical Environments for Intelligent Agents
This addresses the need for versatile environment models in autonomous systems, though it appears incremental as it builds on existing graph-based representations.
The paper tackles the problem of creating an efficient and effective environment model for intelligent agents by proposing a 3-D scene graph, demonstrating its accuracy and applicability in practical applications through comprehensive experiments.
Intelligent agents gather information and perceive semantics within the environments before taking on given tasks. The agents store the collected information in the form of environment models that compactly represent the surrounding environments. The agents, however, can only conduct limited tasks without an efficient and effective environment model. Thus, such an environment model takes a crucial role for the autonomy systems of intelligent agents. We claim the following characteristics for a versatile environment model: accuracy, applicability, usability, and scalability. Although a number of researchers have attempted to develop such models that represent environments precisely to a certain degree, they lack broad applicability, intuitive usability, and satisfactory scalability. To tackle these limitations, we propose 3-D scene graph as an environment model and the 3-D scene graph construction framework. The concise and widely used graph structure readily guarantees usability as well as scalability for 3-D scene graph. We demonstrate the accuracy and applicability of the 3-D scene graph by exhibiting the deployment of the 3-D scene graph in practical applications. Moreover, we verify the performance of the proposed 3-D scene graph and the framework by conducting a series of comprehensive experiments under various conditions.