With the development of fingerprinting-based visual localization technology, a problem with this method is standing out, which is it takes great expenses in fingerprint collection. Recently, a few studies proposed some methods to alleviate this problem. However, the accuracy of the existing method is relatively low under some scenarios such as wide field of vision. In this paper, we propose a novel automatic visual fingerprinting (AVF) method for an indoor visual localization system. We consider the performance of AVF greatly hinges on visual odometry (VO) and ego-motion estimation (EME) block, which are two different ways of estimating fingerprint coordinates. Since both visual odometry and ego-motion estimation model are inaccurate, we build the least square model by second-order cone programming (SOCP). Our SOCP-based method is proposed to deal with the serious cumulative error and the random error introduced by VO and EME model, respectively. The purpose of this paper is improving the accuracy of the database generated by the AVF method under wide field of vision scenarios. Although the time costs are relatively higher than our compared method, fortunately, it is only on the off-line stage. The simulation results show that our method can provide a reliable image-location database with the consumer-grade smartphone camera.
PnP problem is well researched in many fields, such as computer vision. It is considered the fundamental method to solve the key problems of robot SLAM. However, in pedestrian visual localization, uncalibrated PnP (UPnP), specifically PnP with unknown focal length (PnPf) is more suitable for solving the problem. Recently, a few researchers proposed some methods to alleviate this problem. However, the localization accuracy of the existing methods is not satisfied when image pixel noise is larger. In other words, RANSAC should be running before solving the PnPf problem to get less noisy input, which means the localization delay increases inevitably. In this paper, we propose a more robust method for solving the PnPf problem without the help of RANSAC based on Gröbner basis and convex optimization. We build a Gröbner basis solver on the offline stage with one instance in the prime field. Then, we substitute the coefficients with real value and find multiple solutions on the online stage. Finally, we construct a convex optimization program to seek the final robust solution to PnPf problem. The other purpose of this paper is to provide a second-level localization experience for the end-user. The simulation result shows that our method can give a localization solution, which is more reliable than benchmark methods by both synthetic and real data verified.
Recently, deep learning and vision-based technologies have shown their great significance for the prospective development of smart Internet of Vehicle (IoV). When the smart vehicle enters the indoor parking of a shopping mall, the vision-based localization technology can provide reliable parking service. As known, the vision-based technique relies on a visual map without a change in the position of the reference object. Although, some researchers have proposed a few automatic visual fingerprinting (AVF) methods, which are aiming at reducing the cost of building the visual map database. However, the AVF method still costs too much under such a situation, since it is impossible to determine the specific location of the displaced object. Given the smart IoV and the development of deep learning approach, we propose an algorithm for solving the problem based on crowdsourcing and deep learning in this paper. Firstly, we propose a Region-based Fully Convolutional Network (R-FCN) based method with the feedback of crowdsourced images to locate the specific displaced object in the visual map database. Secondly, we propose a method based on quadratic programming (QP) for solving the translation vector of the displaced objects, which finally solves the problem of updating the visual map database. The simulation results show that our method can provide a higher detection sensitivity and correction accuracy as well as the relocation results. It means that our proposed algorithm outperforms the compared one, which is verified by both synthetic and real data simulation.
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