Generation of 3D LWIR thermal maps based on deep learning SLAM: feasibility and evaluation

Machine Learning has played a major role in various applications including Visual Slam and themal image process. In this paper, we discussed the possibility of generating a thermal map using LWIR images and a deep learning-based visual slam network and the value that the thermal map can create. We summarized the advantages and applicability of various deep learning-based visual slams and confirmed the results of nice slam, which generates the most curious Dense map. In order to apply Visual SLAM technology, time series, scene repetition, and images from various angles for one scene are required. However, most LWIR data sets consist of one shot for each scene or are unidirectional driving data. To solve this, we created a scenario using the LWIR driving dataset and created a repetitive route through repetition. RGB-Depth SLAM Mapping was performed on the constructed data set, and the results were evaluated and the limitations of the current approach were discussed. Finally, we summarized future directions for creating stable 3D thermal maps in indoor and outdoor environments by resolving the limitations.