Multi-level Scene Modeling and Matching for Smartphone-Based Indoor Localization

Chen, Lidong; Zou, Yin; Chang, Yaohua; Liu, Jinyun; Lin, Bryan; Zhu, Zhigang

doi:10.1109/ismar-adjunct.2019.00-22

Cited by 6 publications

(1 citation statement)

References 14 publications

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“…Currently, location-based services (LBS) based on indoor positioning show significant potential in large indoor environments such as airports, underground parking lots, shopping malls, and hospitals [1]. For instance, in the context of building management and operation, Bluetooth low-energy-based indoor positioning services can provide precise occupancy information during emergency management, facilitating effective response and rescue operations [2].…”

Section: Introductionmentioning

confidence: 99%

FILNet: Fast Image-Based Indoor Localization Using an Anchor Control Network

Liu,

Huang,

et al. 2023

Sensors

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This paper designs a fast image-based indoor localization method based on an anchor control network (FILNet) to improve localization accuracy and shorten the duration of feature matching. Particularly, two stages are developed for the proposed algorithm. The offline stage is to construct an anchor feature fingerprint database based on the concept of an anchor control network. This introduces detailed surveys to infer anchor features according to the information of control anchors using the visual–inertial odometry (VIO) based on Google ARcore. In addition, an affine invariance enhancement algorithm based on feature multi-angle screening and supplementation is developed to solve the image perspective transformation problem and complete the feature fingerprint database construction. In the online stage, a fast spatial indexing approach is adopted to improve the feature matching speed by searching for active anchors and matching only anchor features around the active anchors. Further, to improve the correct matching rate, a homography matrix filter model is used to verify the correctness of feature matching, and the correct matching points are selected. Extensive experiments in real-world scenarios are performed to evaluate the proposed FILNet. The experimental results show that in terms of affine invariance, compared with the initial local features, FILNet significantly improves the recall of feature matching from 26% to 57% when the angular deviation is less than 60 degrees. In the image feature matching stage, compared with the initial K-D tree algorithm, FILNet significantly improves the efficiency of feature matching, and the average time of the test image dataset is reduced from 30.3 ms to 12.7 ms. In terms of localization accuracy, compared with the benchmark method based on image localization, FILNet significantly improves the localization accuracy, and the percentage of images with a localization error of less than 0.1m increases from 31.61% to 55.89%.

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Section: Introductionmentioning

confidence: 99%