Method of close-range space intersection combining multi-image forward intersection with single hidden layer neural network

• Main Authors: LI Jiatian, WANG Congcong, A Xiaohui, YAN Ling, ZHU Zhihao, GAO Peng
• Format: Article
• Language: zho
• Published: Surveying and Mapping Press 2020-06-01
• Series: Acta Geodaetica et Cartographica Sinica
• Subjects: space intersection; exterior elements; bp neural network; lagrange equation; sparse bundle adjustment
• Online Access: http://html.rhhz.net/CHXB/html/2020-6-736.htm
• ISSN: 1001-1595; 1001-1595

Aiming at the problem that the three-dimensional coordinate solution precision is influenced by the non-linear error, a method of combining multi-image forward intersection with single hidden layer BP neural network is proposed in this paper. The steps are: ①In order to obtain the initial value of the three dimensional coordinates with higher accuracy, the external parameters of the camera are optimized by constructing the Lagrange equation about the world coordinates under the constraint of known real world coordinates of the sample point. ②The single hidden layer BP neural network is trained by using the calculated 3D coordinate and the real 3D coordinate as input and output parameters, respectively. ③The initial three-dimensional coordinate is corrected by brought into the model. Experiments show that: ①In the environmental field of view of the test device, the proposed method outperforms the space intersection, the sparse bundle adjustment and the other classic neural network methods, the maximum deviation is 0.492 7 mm. ②Compared with other classic neural network methods, the network structure of this paper is 3-6-3, the structure is simple and the calculation efficiency is high.