Semantic Hazard Labelling and Risk Assessment Mapping During Robot Exploration

IEEE Access

Jorge Dias¹  Reem Ashour²  Tarek Taha³  Nawaf I. Almoosa⁴  Mohamed Abdelkader⁵ 

[1] Internet of Things Laboratory, Prince Sultan University, Riyadh, Saudi Arabia;Autonomous Robotics Research Centre (ARRC), Technology Innovation Institute (TII), Masdar City, United Arab Emirates;Emirates ICT Innovation Centre (EBTIC), Khalifa University of Science and Technology (KU), Abu Dhabi, United Arab Emirates;Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University of Science and Technology (KU), Abu Dhabi, United Arab Emirates;Robotics &x0026;

关键词: Hazard identification;    mapping;    object classification;    risk assessment;    risk mapping;    semantic mapping;   

DOI  :  10.1109/ACCESS.2022.3148544

来源: DOAJ

【 摘 要 】

This paper proposes an innovative hazard identification and risk assessment mapping model for Urban Search and Rescue (USAR) environments, concentrating on a 3D mapping of the environment and performing grid-level semantic labeling to recognize all hazards types found in the scene and to distinguish their risk severity level. The introduced strategy employs a deep learning model to create semantic segments for hazard objects in 2D images and create semantically annotated point clouds that encapsulate occupancy and semantic annotations such as hazard type and risk severity level. After that, a 3D semantic map that provides situational awareness about the risk in the environment is built using the annotated point cloud. The proposed strategy is evaluated in a realistic simulated indoor environment, and the results show that the system successfully generates a risk assessment map. Further, an open-source package for the proposed approach is provided online for testing and reproducibility.

【 授权许可】

Unknown