<p>Autonomous vehicles and mobile robots usually encounter challenges while perceiving the surrounding environment due to limited field of view. Image stitching technology stitches multiple images to construct a wider field of view, enabling these autonomous agents having a comprehensive environmental perception. While the performance of most image stitching approaches using visible images are affected by illumination changes and parallax problems, infrared cameras are robust and independent of the effect from environmental illumination. This paper proposes an unsupervised deep learning image stitching algorithm that fuses infrared images (ISA-FIR) to resolve parallax problems. The algorithm consists of two parts: a feature flow-based feature contrast algorithm that matches features extracted by a pyramid network and a multi-grid regression network in which infrared images that provide object depth information are fused to support the final stitching. In addition, most image stitching datasets nowadays contain images captured from one perspective, while real-world images collected by moving autonomous vehicles and robots usually contain significant parallax information that will affect the final stitching performance. To fill in the gap in datasets with parallax-tolerant images and validate our method, we construct a four-wheel robot and propose a novel dataset for image stitching. The dataset contains pairs of visible light images and infrared images of different scenes from different perspectives captured by the robot. Experimental results suggest that our method outperforms conventional and state-of-the-art image stitching approaches in all scenes, proving the advantages of our ISA-FIR in image stitching tasks.</p>