Real-Time Panorama Stitching Method for UAV Sensor Images Based on the Feature Matching Validity Prediction of Grey Relational Analysis
Xiaoyue Ji, Xiaojia Xiang, Jian Xin Huang · 2018
Images obtained by unmanned aerial vehicle (UAV) are superior in many aspects such as convenience, low cost and so on. However, the obtained attitude of the UAV is usually not accurate, which further leads to the decrease of efficiency and accuracy of the algorithm based on the position and attitude information. Despite the algorithm based on image feature can get a more accurate result, it usually requires a longer time to process the images and even can bring a large cumulative error when continuous image is stitched. This paper presents a new method for stitching panoramas from UAV's images which is mainly composed of three steps. Firstly, once the images have been obtained, global motion model for UAV aerial photography can be used to establish a predicted region. Second, features in the onboard images are matched by using SIFT algorithm in this region. Finally, the accuracy of image mosaic is predicted through the Grey Relational Analysis. Then, according to the result, the algorithm process can adjust itself automatically to meet the needs of different types of sensor image mosaic. We demonstrate through flight experiments that compared with conventional approach our method can stitch images accurately and rapidly and produce high-quality panoramas.