Ceiling-Based Visual Positioning for an Indoor Mobile Robot With Monocular Vision

Xu, De; Han, Liwei; Tan, Min; Li, Youfu

doi:10.1109/tie.2009.2012457

Cited by 106 publications

(12 citation statements)

References 27 publications

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“…In this case we would not be able to ascertain the circularity of the estimation cloud. Hence, as complementary information, we also define a circularity index (CI) as: (27) where λ i min and λ i max are, respectively, the minimum and maximum value of the eigenvalues pair {λ 1 , λ 2 } appearing in (2). This index is evaluated at every position in the BLC grid.…”

Section: Infrared Measurementsmentioning

confidence: 99%

“…A comprehensive review where different approaches and their features and performance can be found in [26]. In many works different sensors are used in a cooperative (not fusion) way, as in [20] with a camera and a LIDAR (Laser Imaging Detection and Ranging) or in [27] with camera and odometry, both for robot navigation applications, or [24] where ArUco markers [28] (widely known encoded markers, also used in this work) and an IMU cooperate for a drone navigation and landing application. There is no dominant approach in fusion of cameras with other sensors in positioning and navigation applications.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Indoor Positioning Approach Based on Fusion of Cameras and Infrared Sensors

Martín-Gorostiza¹,

García-Garrido²,

Pizarro³

et al. 2019

Preprint

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A method for infrared and cameras sensor fusion, applied to indoor positioning in intelligent spaces, is proposed in this work. The fused position is obtained with a maximum likelihood estimator from infrared and camera independent observations. Specific models are proposed for variance propagation from infrared and camera observations (phase shifts and image respectively) to their respective position estimates and to the final fused estimation. Model simulations are compared with real measurements in a setup designed to validate the system. The difference between theoretical prediction and real measurements  is between 0.4 cm (fusion) and  2.5 cm (camera), within a 95% confidence margin. The positioning precision is in the cm level (sub-cm level can be achieved at most tested positions) in a 4x3 m locating cell with 5 infrared detectors on the ceiling and one single camera, at distances from target up to 5 m and 7 m respectively. Due to the low cost system design and the results observed, the system is expected to be feasible and scalable to large real spaces.

show abstract

Section: Infrared Measurementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Indoor Positioning Approach Based on Fusion of Cameras and Infrared Sensors

Martín-Gorostiza¹,

García-Garrido²,

Pizarro³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…A comprehensive review where different approaches and their features and performance can be found in [26]. In many works different sensors are used in a cooperative (not fusion) way, as in [20] with a camera and a Laser Imaging Detection and Ranging (LIDAR) or in [27] with camera and odometry, both for robot navigation applications, or [24] where ArUco markers [28] (widely known encoded markers, also used in this work) and an IMU cooperate for a drone navigation and landing application. There is no dominant approach in fusion of cameras with other sensors in positioning and navigation applications.…”

Section: Introductionmentioning

confidence: 99%

An Indoor Positioning Approach Based on Fusion of Cameras and Infrared Sensors

Martín-Gorostiza

García-Garrido

Pizarro

et al. 2019

Sensors

View full text Add to dashboard Cite

A method for infrared and cameras sensor fusion, applied to indoor positioning in intelligent spaces, is proposed in this work. The fused position is obtained with a maximum likelihood estimator from infrared and camera independent observations. Specific models are proposed for variance propagation from infrared and camera observations (phase shifts and image respectively) to their respective position estimates and to the final fused estimation. Model simulations are compared with real measurements in a setup designed to validate the system. The difference between theoretical prediction and real measurements is between 0.4 cm (fusion) and 2.5 cm (camera), within a 95% confidence margin. The positioning precision is in the cm level (sub-cm level can be achieved at most tested positions) in a 4 × 3 m locating cell with 5 infrared detectors on the ceiling and one single camera, at distances from target up to 5 m and 7 m respectively. Due to the low cost system design and the results observed, the system is expected to be feasible and scalable to large real spaces.

show abstract

“…In addition, the camera's lens may be occluded by dynamic objects (e.g., pedestrians) or static objects (e.g., furniture), which seriously limits the application of off-board visual localization method in indoor environments. On-board visual localization method with cameras directly mounted on the robot is an additional localization method that shows great promise [21][22][23]. The key objective is to recognize natural or artificial landmarks with known locations and then calculate the distances between the robot and the landmarks; consequently, the robot's location can be readily determined using trilateration.…”

Section: Introductionmentioning

confidence: 99%

FVO: floor vision aided odometry

Kang

Qin

2018

Sci. China Inf. Sci.

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In many indoor scenarios, such as restaurants, laboratories, and supermarkets, the planar floors are covered with rectangular tiles. We realized that the abundant parallel lines and crossing points formed by tile joints can be used as natural features to assist indoor localization, and thus we propose a novel indoor localization method for mobile robots by fusing odometry and monocular vision. The method comprises three steps. First, the heading and location of the mobile robot are approximately estimated by odometry based on incremental encoders. Second, with the aid of a camera, the lens of which points vertically toward the floor, the odometric heading estimation can be corrected by detecting the relative angle between the robot's heading and the tile joints. Third, the odometric location estimation is corrected by detecting the perpendicular distance between the image center and the tile joints. As compared with the existing indoor localization methods, the proposed method, called floor vision aided odometry, is not only relatively low in economic cost and computational complexity, but also relatively high in accuracy and robustness. The effectiveness of this method is verified by a real-world experiment based on a differential-drive wheeled mobile robot.

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Ceiling-Based Visual Positioning for an Indoor Mobile Robot With Monocular Vision

Cited by 106 publications

References 27 publications

An Indoor Positioning Approach Based on Fusion of Cameras and Infrared Sensors

An Indoor Positioning Approach Based on Fusion of Cameras and Infrared Sensors

An Indoor Positioning Approach Based on Fusion of Cameras and Infrared Sensors

FVO: floor vision aided odometry

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