VISEL: A visual and magnetic fusion‐based large‐scale indoor localization system with improved high‐precision semantic maps

Li, Ning; Tu, Weiping; Ai, Haojun; Deng, Huimin; Tao, Jingjie; Hu, Tan; Sun, Xu

doi:10.1002/int.22913

Cited by 5 publications

(1 citation statement)

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“…To reduce the storage requirement, only roof corners are considered in [ 13 ]. In [ 14 ], semantic segmentation technology is combined with the location algorithm for better performance, but much computing resource is required by the semantic segmentation process. No extra effort is needed to reconstruct the environment if using these already existing indoor objects.…”

Section: Introductionmentioning

confidence: 99%

Indoor Location Technology with High Accuracy Using Simple Visual Tags

Gao

2023

Sensors

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To achieve low-cost and robustness, an indoor location system using simple visual tags is designed by comprehensively considering accuracy and computation complexity. Only the color and shape features are used for tag detection, by which both algorithm complexity and data storage requirement are reduced. To manage the nonunique problem caused by the simple tag features, a fast query and matching method is further presented by using the view field of the camera and the tag azimuth. Then, based on the relationship analysis between the spatial distribution of tags and location error, a pose and position estimation method using the weighted least square algorithm is designed and works together with the interactive algorithm by the designed switching strategy. By using the techniques presented, a favorable balance is achieved between the algorithm complexity and the location accuracy. The simulation and experiment results show that the proposed method can manage the singular problem of the overdetermined equations effectively and attenuate the negative effect of unfavorable label groups. Compared with the ultrawide band technology, the location error is reduced by more than 62%.

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Section: Introductionmentioning

confidence: 99%