Design of coded reference labels for indoor optical navigation using monocular camera

Anwar, Qaiser; Malik, Aqsa; Thörnberg, Benny

doi:10.1109/ipin.2013.6817925

Cited by 8 publications

(10 citation statements)

References 7 publications

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“…There are existing tags designed for different purposes. Examples of existing tags are shown in Figure 4 and a summary of such tags is listed as follows: [11][12][13][14][15][16][17][18][19]31,32 QR-Code: it uses a quadrilateral visual tag where information is stored in a binary pixel matrix of 1817-7089 characters, depending on the alphabet used. A main problem of its visual tag is its rather low detection speed, due to the large amount of information stored in it.…”

Section: Tag Designmentioning

confidence: 99%

“…Since each tag has its own special features (or a combination of features), an exact mapping between tags and IDs can be established. To identify each tag, a neural network with manually made features is used in Anwar et al 19 Those manually made features are specially designed for their tags and are not guaranteed to be the best features. To improve the performance, a VGG16-based CNN 42 is trained to predict the ID of the segmented tag.…”

Section: Tag Classificationmentioning

confidence: 99%

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Image-tag-based indoor localization using end-to-end learning

Alarfaj

Liu

et al. 2021

International Journal of Distributed Sensor Networks

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Image or feature matching-based indoor localization still faces many technical challenges. Image-tag-based schemes using pose estimation are accurate and robust, but they still cannot be deployed widely because their performance degrades significantly when the tag-camera distance is large, which requires densely distributed tags, and the designed system generally is specific to some special tags and lenses. Also, the lens distortion degrades the performance appreciably and is difficult to correct, especially for the wide-angle lenses. This article develops an image-tag-based indoor localization system using end-to-end learning to overcome these issues. It is a deep learning–based system that can learn the mapping from the original tag image to the final 2D location directly from training examples through self-learned features. It achieves consistent performance even when the tag-camera distance is large or when the image has a low resolution. The mapping learned by the deep learning model factors in all kinds of distortions without requiring any distortion estimation. The tag design is based on shape features to make it robust to lighting changes. The system can be easily adapted to new lenses/cameras and/or new tags. Thus, it facilitates easy and rapid deployment without requiring knowledge from domain experts. A drawback of the general deep learning model is its high computational requirements. We discuss practical solutions to enable real-time applications of the proposed scheme even when it is running on a mobile or embedded device. The performance of the proposed scheme is evaluated via a set of experiments in a real setting and has achieved less than 20 cm of positioning errors.

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Section: Tag Designmentioning

confidence: 99%

Section: Tag Classificationmentioning

confidence: 99%

Image-tag-based indoor localization using end-to-end learning

Alarfaj

Liu

et al. 2021

International Journal of Distributed Sensor Networks

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“…In these systems, the computational load required for the images identification is very high. Finally, systems that use the reference from deployed coded targets technique calculate the unknown position by exploiting dedicated coded markers or labels placed in the environment [24], [25].…”

Section: Indoor Localization and Tracking Systemsmentioning

confidence: 99%

A survey on indoor positioning systems

Mainetti

Patrono

Sergi

2014

2014 22nd International Conference on Software, Telecommunications and Computer Networks (SoftCOM)

264

136

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This paper aims to provide the reader with a review of the main technologies explored in the literature to solve the indoor localization issue. Furthermore, some systems that use these enabling technologies in real-world scenarios are presented and discussed. This could deliver a better understanding of the state-of-the-art and motivate new research efforts in this promising field. Finally, focusing on one of the major challenges in the indoor localization field, i.e., the indoor animal tracking, existing indoor tracking systems have been reviewed and compared by analyzing advantages and drawbacks.

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“…Coded reference labels on walls are used to locate indoor images. These methods obtain a high accuracy from decimeters to meters [ 33 ]. The coded labels are needed to be measured by total station, and not convenient to be used.…”

Section: Introductionmentioning

confidence: 99%

Accurate Smartphone Indoor Visual Positioning Based on a High-Precision 3D Photorealistic Map

Liu

Zheng

et al. 2018

Sensors

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Indoor positioning is in high demand in a variety of applications, and indoor environment is a challenging scene for visual positioning. This paper proposes an accurate visual positioning method for smartphones. The proposed method includes three procedures. First, an indoor high-precision 3D photorealistic map is produced using a mobile mapping system, and the intrinsic and extrinsic parameters of the images are obtained from the mapping result. A point cloud is calculated using feature matching and multi-view forward intersection. Second, top-K similar images are queried using hamming embedding with SIFT feature description. Feature matching and pose voting are used to select correctly matched image, and the relationship between image points and 3D points is obtained. Finally, outlier points are removed using P3P with the coarse focal length. Perspective-four-point with unknown focal length and random sample consensus are used to calculate the intrinsic and extrinsic parameters of the query image and then to obtain the positioning of the smartphone. Compared with established baseline methods, the proposed method is more accurate and reliable. The experiment results show that 70 percent of the images achieve location error smaller than 0.9 m in a 10 m × 15.8 m room, and the prospect of improvement is discussed.

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Design of coded reference labels for indoor optical navigation using monocular camera

Cited by 8 publications

References 7 publications

Image-tag-based indoor localization using end-to-end learning

Image-tag-based indoor localization using end-to-end learning

A survey on indoor positioning systems

Accurate Smartphone Indoor Visual Positioning Based on a High-Precision 3D Photorealistic Map

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