A Mobile Robot Application for Constructing Semantic and Metric Maps of Search and Rescue Arenas with Point-Based Deep Learning

Kocaoğlu M., Işıkdemir Y. E., Uzun M. A., Taş M. O., Turgut K., Kaleci B.

Journal of Scientific, Technology and Engineering Research, vol.2, no.1, pp.11-22, 2021 (Peer-Reviewed Journal)


This study aims to create semantic and metric maps of a post-disaster indoor environment similar to standard the National Institute of Standards and Technology (NIST) search and rescue test arenas that first-responders can easily read. We prefer to use point cloud data acquired with an RGB-D camera since it does not be affected by post-disaster environments’ dusty and dull nature. Besides, each point cloud data is processed separately so that the semantic and metric maps grow incrementally. The Dynamic Graph Convolutional Neural Network (DGCNN) is used to classify points as sematic categories such as walls, terrain, and inclined and straight ramps. RTAB-Map and the semantic map are utilized to generate the octree-based 3D metric map. The experiments are conducted in a simulated environment modelled with Gazebo similar to NIST test arenas to show the effectiveness of the proposed method.