A SOCP-Based Automatic Visual Fingerprinting Method for Indoor Localization System

Tan

et al. 2021

IEEE Access

Self Cite

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.

Section: Back Substitutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Robust Visual Localization Method With Unknown Focal Length Camera

Tan

et al. 2021

IEEE Access

Self Cite

“…However, these devices need to be customized specially, which are also expensive. To solve this problem, Farhang et al proposed an automatic visual fingerprinting method based on the consumer-grade device in [8], which was further improved in our previous work [9]. These methods have well solved the visual fingerprinting problem in different ways.…”

Section: A Background and Significancementioning

confidence: 99%

A Visual Fingerprint Update Algorithm Based On Crowdsourced Localization And Deep Learning For Smart IoV

Sun

2021

Preprint

Self Cite

Recently, the deep learning and vision-based technologies has 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 situation, since it is impossible to determine the specific location of the displaced object. In view of the smart IoV and the development of deep learning approach, we propose a crowdsourcing and deep learning based algorithm for solving the problem 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.

“…However, these devices need to be customized specially, which is also expensive. To solve this problem, Farhang et al proposed an automatic visual fingerprinting method based on the consumer-grade device in [13], which was further improved in our previous work [14]. These methods have well solved the visual fingerprinting problem in different ways.…”

mentioning

confidence: 99%

A visual fingerprint update algorithm based on crowdsourced localization and deep learning for smart IoV

EURASIP J. Adv. Signal Process.

Sun

et al. 2021

Self Cite

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.