Location Recognition of Indoor Firefighting Facilities based on RGB-D Camera and 3D LiDAR

Abstract

Recently, as fires frequently occur in large buildings, digital twin (DT) technology that enables remote and real-time monitoring and control similar to the real world environment is being studied as a disaster response technology in buildings. In order to use DT technology, it is essential to collect the spatial data of actual building indoor environments and firefighting facilities. This study proposes an indoor spatial data collecting system that can generate the modeling data inside the building and location data of firefighting facilities using laser imaging detection and ranging (LiDAR) and RGB-D cameras. First, point clouds from three-dimensional (3D) LiDAR and the FAST-LIO2 (Fast LiDAR-Inertial Odometry) algorithm are used to obtain odometry information in an indoor environment. The firefighting facilities located inside the building are detected using RGB images and the deep learning model Faster region-based convolutional neural network (R-CNN) with Inception V2 architecture trained using RGB images of four types of firefighting facilities: fire extinguishers, fire hydrants, exit signs, and fire detectors. When a firefighting facility is detected, the relative distance between the RGB-D camera and the firefighting facility is calculated through the depth image and intrinsic parameters of the RGB-D camera. Afterwards, odometry information obtained from FAST-LIO2 and the relative distance are combined to obtain the 3D location of the firefighting facility. Point clouds of the FAST-LIO2 algorithm are then converted into models of the building indoor environment. Through this method, spatial data of an actual building can be constructed and used with DT technology.