Vision-based Recognition Algorithm for Up-To-Date Indoor Digital Map Generations at Damaged Buildings

When firefighters are engaged in search and rescue missions inside a building at a risk of collapse, they have difficulty in field command and rescue because they can only simply monitor the situation inside the building utilizing old building drawings or robots. To propose an efficient solution for fast search and rescue work of firefighters, this study investigates the generation of up-to-date digital maps for disaster sites by tracking the collapse situation, and identifying the information of obstacles which are risk factors, using an artificial intelligence algorithm based on low-cost robots. Our research separates the floor by using the mask regional convolutional neural network (R-CNN) algorithm, and determines whether the passage is collapsed or not. Then, in the case of a passage that can be searched, the floor pattern of the obstacles that exist on the floor that has not collapsed is analyzed, and obstacles are searched utilizing an image processing algorithm. Here, we can detect various unknown as well as known obstacles. Furthermore, the locations of obstacles can be estimated using the pixel values up to the bounding box of an existing detected obstacle. We conduct experiments using the public datasets collected by Carnegie Mellon university (CMU) and data collected by manipulating a low-cost robot equipped with a smartphone while roaming five buildings in a campus. The collected data have various floor patterns for objectivity and obstacles that are different from one another. Based on these data, the algorithm for detecting unknown obstacles of a verified study and estimating their sizes had an accuracy of 93%, and the algorithm for estimating the distance to obstacles had an error rate of 0.133. Through this process, we tracked collapsed passages and composed up-to-date digital maps for disaster sites that include the information of obstacles that interfere with the search and rescue work.