A PCLR-GIST Algorithm for Fast Image Retrieval in Visual Indoor Localization System

Yin, Xiliang; Ma, Lin; Tan, Xiaoyang

doi:10.1109/vtcspring.2018.8417794

Cited by 3 publications

(2 citation statements)

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“…Thus, we use f r instead of f g . It is a reference value, which could estimate from the image information.t 3 can be calculated by (5) and (8). Then, t 1 andt 2 are reached by equations in (7), respectively.…”

Section: Back Substitutionmentioning

confidence: 99%

“…It proved more efficient even in some feature-barren environments [7]. With the feature in the query image and its correspondence in the visual map database, the relationship could map by algorithms, such as [8]- [10]. Finally, the correspondences will be used as a known condition to solve the PnP problem, whose solution is a fine localization result for the querying user.…”

Section: Introductionmentioning

confidence: 99%

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A Robust Visual Localization Method With Unknown Focal Length Camera

Yin

Tan

et al. 2021

IEEE Access

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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.

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Section: Back Substitutionmentioning

confidence: 99%