In recent years, longwave infrared (LWIR) cameras have become potential in visual simultaneous localization and mapping (SLAM) research since the delivered thermal images can provide information beyond the visible spectrum and are robust to environment illumination. However, due to modality differences, SLAM methods designed for visible cameras cannot be directly applied to thermal data. In this paper, we propose a thermal-inertial SLAM method for all-day autonomous systems. To overcome the challenge of the thermal data association, the proposed method represents several improvements, including singular-value-decomposition-based (SVD-based) image processing and ThermalRAFT tracking methods. Based on the characteristics of the thermal images, the SVD-based image processing method can exploit the fixed noise pattern of thermal images and enhance the image quality to improve the performance of subsequent steps, including thermal feature extraction and loop detection. To achieve real-time and robust feature tracking, we develop ThermalRAFT, an efficient optical flow network with iterative optimization. Moreover, the system introduces a bag-of-words-based loop detection method to maintain global consistency in long-term operation. The experimental results demonstrate that the proposed method can provide competitive performance in indoor and outdoor environments and is robust under challenging illumination conditions.