Smartphone Photography Visual Localization Based on an Improved Siamese Neural Network
Qiaolin Pu, Rui Cai, Mu Zhou, Kaiyu Luo, Yiran Miao · IEEE Internet of Things Journal · 2024
With the increasing popularity of smartphones, smartphone photography has become convenient and common in daily life, thus making visual localization technology receive widespread attention. Due that monocular cameras are mostly used on smartphones, the depth information from a single image cannot be obtained, so the image-matching-based localization technique is widely adopted. However, this method has the problems of high time consumption and vulnerable to environmental interference. Therefore, this article proposes a low overhead and robust indoor image positioning method, which mainly consists of two modules. First, an improved siamese neural network framework is introduced to train the similarity metric model between images, which significantly reduces the workload of labeling for large amounts of sample data. Moreover, it improves the robustness when the target environment contains similar image features in the matching stage. Second, to further estimate the user’s fine location, an adaptive random sample consensus algorithm is proposed to optimize the fundamental matrix in the classical EightPoint method, which could efficiently eliminate the matching outliers to solve the camera attitude, and dynamically adapt to more complex and changeable data situations. A large number of experimental results show that the positioning performance of this scheme is better than traditional schemes, and the average positioning error of 0.50 m can be achieved.