On-board GNSS/IMU assisted feature extraction and matching for oblique UAV images

Abstract

Feature extraction and matching is a crucial task in the fields of computer vision and photogrammetry. Even though wide researches have been reported, some issues are still existing for oblique images. This paper exploits the use of on-board GNSS/IMU (Global Navigation Satellite System/Inertial Measurement Unit) data to achieve efficient and reliable feature extraction and matching for oblique unmanned aerial vehicle (UAV) images. Firstly, rough POS (Positioning and Orientation System) is calculated for each image with cooperation of on-board GNSS/IMU data and camera installation angles, which enables image rectification and footprint calculation. Secondly, two robust strategies, including the geometric rectification and tile strategy, are considered to address the issues caused by perspective deformations and to relieve the side-effects of image down-sampling. According to the results of individual performance evaluation, four combinations of these two strategies are designed and comprehensively compared in BA (Bundle Adjustment) experiments by using a real oblique UAV dataset. The results reported in this paper demonstrate that the solution with the tiling strategy is superior to the other solutions in terms of efficiency, completeness and accuracy. For feature extraction and matching of oblique UAV images, it is proposed to combine the tiling strategy with existing workflows to achieve an efficient and reliable solution.