EAMT-SLAM: Edge-Aided Monocular Thermal Sensor SLAM in Low-Illumination Environments

In low-illumination environments, visible light images significantly degrade, and the simultaneous localization and mapping (SLAM) system based on visible cameras fails. In contrast, thermal sensors are not affected by scene lighting conditions and have become a promising alternative. However, due to the problems of low contrast, high noise, and data interruption in thermal images, there are still many challenges in implementing thermal sensor SLAM. Therefore, this article proposes an edge-aided monocular thermal sensor SLAM system, called EAMT-SLAM, suitable for low illumination environments. First, the edge provides more feature associations to assist the SLAM system in initializing quickly and accurately. Subsequently, the saliency map is calculated based on the strong edge in the thermal image, and the weights are reasonably assigned to the feature points in the optimization process of pose estimation to obtain more accurate results. Finally, this article proposes an edge-guided loop detection method, which achieves higher accuracy and real-time performance compared with the appearance-based method, effectively ensuring the global consistency of SLAM. In addition, this article proposes a new thermal image processing method that can enhance image details and remove complex noise in real time, significantly improve the quality of thermal images, and broaden the application scope of the EAMT-SLAM system. The experimental results show that our method can obtain the highest quality thermal image under quantitative and qualitative evaluation, complete accurate loop detection, and finally realize accurate pose estimation in low-illumination environments.