A weighted KNN epipolar geometry-based approach for vision-based indoor localization using smartphone cameras

Sadeghi, Hamed; Valaee, Shahrokh; Shirani, Shahram

doi:10.1109/sam.2014.6882332

Cited by 31 publications

(25 citation statements)

References 13 publications

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“…Therefore, more and more studies have focused on the field of localization and navigation based on visual methods using smartphones [9][10][11][12][13][14][15][16][17]. Vision-based localization takes advantage of image retrieval to search for the similar geo-tagged database images for a query image, and then the location of the query image can be estimated.…”

Section: Introductionmentioning

confidence: 99%

“…Vision-based localization takes advantage of image retrieval to search for the similar geo-tagged database images for a query image, and then the location of the query image can be estimated. In the phase of visual localization, the homography matrix [10] and the epipolar constraint [11] are typically used to calculate the location of the query images based on the positions of the matched database images. Therefore, the performance of the visual localization system depends on the position accuracy of the database images.…”

Section: Introductionmentioning

confidence: 99%

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Visual Map Construction Using RGB-D Sensors for Image-Based Localization in Indoor Environments

Feng

Tan

2017

Journal of Sensors

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RGB-D sensors capture RGB images and depth images simultaneously, which makes it possible to acquire the depth information at pixel level. This paper focuses on the use of RGB-D sensors to construct a visual map which is an extended dense 3D map containing essential elements for image-based localization, such as poses of the database camera, visual features, and 3D structures of the building. Taking advantage of matched visual features and corresponding depth values, a novel local optimization algorithm is proposed to achieve point cloud registration and database camera pose estimation. Next, graph-based optimization is used to obtain the global consistency of the map. On the basis of the visual map, the image-based localization method is investigated, making use of the epipolar constraint. The performance of the visual map construction and the image-based localization are evaluated on typical indoor scenes. The simulation results show that the average position errors of the database camera and the query camera can be limited to within 0.2 meters and 0.9 meters, respectively.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Visual Map Construction Using RGB-D Sensors for Image-Based Localization in Indoor Environments

Feng

Tan

2017

Journal of Sensors

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“…The basic idea of the epipolar geometrybased method for location recognition is proposed in [8] by Sadeghi for the indoor environment. But in Sadeghi's paper, only the indoor environment is considered and the timeliness of the image retrieval is not mentioned.…”

Section: Introductionmentioning

confidence: 99%

“…Rt is the slope factor of the connection line between the projection point of the database camera center and the projection point of the query camera center on the ground plane [8].…”

mentioning

confidence: 99%

“…In the 2D situation (ground plane), we consider   connection line (such as line 13 l or 23 l in Figure 5) which connects the projection points of the database camera center and the query camera center [8]. Since we know the locations of database cameras (such as location point 1 O  and 2 O  in Figure 5, we can determine the connection lines with the slope factor and the database camera locations.…”

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confidence: 99%

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Visual Location Recognition Based on Coarse-to-Fine Image Retrieval and Epipolar Geometry Constraint for Urban Environment

Feng¹,

Ma²,

Tan³

et al. 2016

IJSIP

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Parameters Optimization for KFKM Clustering Algorithm Based on WiFi Indoor Positioning

Liu

et al. 2018

Human Centered Computing

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A weighted KNN epipolar geometry-based approach for vision-based indoor localization using smartphone cameras

Cited by 31 publications

References 13 publications

Visual Map Construction Using RGB-D Sensors for Image-Based Localization in Indoor Environments

Visual Map Construction Using RGB-D Sensors for Image-Based Localization in Indoor Environments

Visual Location Recognition Based on Coarse-to-Fine Image Retrieval and Epipolar Geometry Constraint for Urban Environment

Parameters Optimization for KFKM Clustering Algorithm Based on WiFi Indoor Positioning

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