TSM: Topological Scene Map for Representation in Indoor Environment Understanding

• Published in: IEEE access Vol. 8; pp. 185870 - 185884
• Main Authors: Liao, Zhiyong, Zhang, Yu, Luo, Junren, Yuan, Weilin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Indoor environments; Measurement; Navigation; Representations; Robotics; Robots; Scene analysis; Scene graph generation; semantic map; Semantics; Task analysis; topological map construction; Topology
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3029324

In the field of robotics, it is crucial to obtain a comprehensive semantic understanding of a scene for many applications. Based on the behavioral topological map and scene graph, we propose to employ a semantic map named Topological Scene Map (TSM) for representation in indoor environment understanding. The behavioral topological map we constructed expresses the spatial connection relations and semantically describes the navigation behavior between adjacent topological nodes. The scene graph promotes the TSM to record the objects that appear in the scene and the relations between objects. The addition of spatial and semantic relations makes the expression of the scene more specific, which improves the robot's abilities of scene understanding and human-robotic interaction. In this article, we design a method for topological map construction and apply a novel approach to generate a scene graph from RGB-D data. The semantic representation of the environment generated in the experiments verifies that the TSM construction framework models the scene efficiently and the TSM is conducive to the realization of human-robotic interaction.