Automatic multi-image stitching for concrete bridge inspection by combining point and line features |
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| Affiliation: | 1. Department of Communications and Computer Engineering, University of Malta, Msida, Malta;2. Engineering Department, CERN, Meyrin, Switzerland;1. Jiangsu Vocational Institute of Commerce, China;2. Army Engineering University of PLA, China;3. Nanjing University, China;1. School of Electronic and Control Engineering, Chang''an University, Xi''an 710064, Shaanxi, China;2. Key Laboratory for Old Bridge Detection and Reinforcement Technology of Ministry of Transportation, Chang''an University, Xi''an 710064, Shaanxi, China;1. School of Electrical Engineering, North China University of Science and Technology, Tangshan , Hebei 063009, PR China;2. Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, Alberta T2N 1N4, Canada\n;3. School of Automation Engineering, University of Science & Technology Beijing, Beijing 100083, PR China;4. Merchant Marine College, Shanghai Maritime University Shanghai 201306, PR China;5. School of Mechanical Engineering, Yangtze University, Jingzhou, Hubei 434023, PR China;1. Southern University of Science and Technology, No.1088 Xueyuan Avenue, Shenzhen City, Guangdong Province, 518055, China;2. Taizhou Research Institute of Southern University of Science and Technology, No.638 Donghuan Avenue, Taizhou City, Zhejiang Province, 317700, China;3. Southern Industrial Technology Research Institute (Shenzhen), Jingang Business Building, Shenzhen City, Guangdong Province, 518100, China;4. Peking Union Medical College Hospital, No.1 Shuaifuyuan, Beijing, 100730, China |
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| Abstract: | Most of the current techniques for concrete bridge inspection are based on human visual interpretation, which often is dangerous and time-consuming. To address this problem, we introduce in this paper a newly developed vehicle-based robot inspection system that can automatically capture thousands of bottom surface images with a group of high-resolution industrial cameras, which are then stitched into a single composite image. However, traditional image stitching methods generally fail with large drift due to the great number (more than 2000) and sparse texture of linearly distributed images in sequence. Therefore, a novel image stitching method was developed for our robot inspection system, which combines both the 2D image point features and the 3D line features to reduce the drift. First, the bottom surface images are arranged into different strips based on their acquisition order and rough poses, and images in a single strip are divided into several groups. Then, the proposed image stitching method is performed in a bottom-up way, as follows: 1) the images within a single group initially are aligned via their point and line features; 2) the groups within a single strip are then stitched together via a homographic refinement procedure; 3) the strips are aligned into a single composite image that completely covers the bottom surface of the bridge; and 4) after all the stitching procedure are complete, a multi-band blending algorithm is applied to generate the mosaicked panorama as seamlessly as possible. The experimental results on a set of representative images acquired from the bottom surfaces of a real bridge demonstrate the capabilities and the limitations of the proposed approach. |
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