Self-Supervised Depth and Ego-Motion Estimation for Monocular Thermal Video Using Multi-Spectral Consistency Loss
| Autor: | Ukcheol Shin, Kyunghyun Lee, Seokju Lee, In So Kweon |
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| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Control and Optimization Computer Vision and Pattern Recognition (cs.CV) Mechanical Engineering Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Biomedical Engineering Computer Science Applications Human-Computer Interaction Computer Science - Robotics Artificial Intelligence Control and Systems Engineering Computer Vision and Pattern Recognition Robotics (cs.RO) |
| Zdroj: | IEEE Robotics and Automation Letters. 7:1103-1110 |
| ISSN: | 2377-3774 |
| DOI: | 10.1109/lra.2021.3137895 |
| Popis: | A thermal camera can robustly capture thermal radiation images under harsh light conditions such as night scenes, tunnels, and disaster scenarios. However, despite this advantage, neither depth nor ego-motion estimation research for the thermal camera have not been actively explored so far. In this paper, we propose a self-supervised learning method for depth and ego-motion estimation from thermal images. The proposed method exploits multi-spectral consistency that consists of temperature and photometric consistency loss. The temperature consistency loss provides a fundamental self-supervisory signal by reconstructing clipped and colorized thermal images. Additionally, we design a differentiable forward warping module that can transform the coordinate system of the estimated depth map and relative pose from thermal camera to visible camera. Based on the proposed module, the photometric consistency loss can provide complementary self-supervision to networks. Networks trained with the proposed method robustly estimate the depth and pose from monocular thermal video under low-light and even zero-light conditions. To the best of our knowledge, this is the first work to simultaneously estimate both depth and ego-motion from monocular thermal video in a self-supervised manner. Comment: 8 pages, Accepted by IEEE Robotics and Automation Letters (RA-L) with ICRA 2022 option |
| Databáze: | OpenAIRE |
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