Binocular Vision Object Positioning Method for Robots Based on Coarse-fine Stereo Matching

Ma, Weiping; Li, Wenxin; Cao, Peng-Xia

doi:10.1007/s11633-020-1226-3

Cited by 15 publications

(7 citation statements)

References 8 publications

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“…Through further analysis of the information, the contour information of the target can be obtained, such as whether the target is the car, the pedestrian, or just the railing [19,20]. Through the above process, the recognition of the target obstacle is realized.…”

Section: Obstacle Identificationmentioning

confidence: 99%

Automatic Roadblock Identification Algorithm for Unmanned Vehicles Based on Binocular Vision

Fang

Guan

2021

Wireless Communications and Mobile Computing

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In order to improve the accuracy of automatic obstacle recognition algorithm for driverless vehicles, an automatic obstacle recognition algorithm for driverless vehicles based on binocular vision is constructed. Firstly, the relevant parameters of the camera are calibrated around the new car coordinate system to determine the corresponding obstacle position of the vehicle. At the same time, the three-dimensional coordinates of obstacle points are obtained by binocular matching method. Then, the left and right cameras are used to capture the feature points of obstacles in the image to realize the recognition of obstacles. Finally, the experimental results show that for obstacle 1, the recognition error of the algorithm is 0.03 m; for obstacle 2, the recognition error is 0.02 m; for obstacle 3, the recognition error is 0.01 m. The algorithm has small recognition error. The vehicle coordinate system is added in the camera calibration process, which can accurately measure the relative position information between the vehicle and the obstacle.

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Section: Obstacle Identificationmentioning

confidence: 99%

Automatic Roadblock Identification Algorithm for Unmanned Vehicles Based on Binocular Vision

Fang

Guan

2021

Wireless Communications and Mobile Computing

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“…Binocular vision needs to accurately know the spatial position relationship between the two cameras, collect two images from different perspectives, and then calculate the 3D coordinates of a target according to the pixel difference of the two images through the triangulation principle. Stereo matching is complex [ 32 ]. Monocular vision locating only needs to estimate the target orientation and distance information through the inherent performance parameters of the camera, which is low-cost and easy to implement.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Solution for Moving-Target Detection and Tracking While Only Using a Monocular Camera

Wang

et al. 2023

Sensors

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This paper focuses on moving-target detection and tracking in a three-dimensional (3D) space, and proposes a visual target tracking system only using a two-dimensional (2D) camera. To quickly detect moving targets, an improved optical flow method with detailed modifications in the pyramid, warping, and cost volume network (PWC-Net) is applied. Meanwhile, a clustering algorithm is used to accurately extract the moving target from a noisy background. Then, the target position is estimated using a proposed geometrical pinhole imaging algorithm and cubature Kalman filter (CKF). Specifically, the camera’s installation position and inner parameters are applied to calculate the azimuth, elevation angles, and depth of the target while only using 2D measurements. The proposed geometrical solution has a simple structure and fast computational speed. Different simulations and experiments verify the effectiveness of the proposed method.

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“…The precision of the final reconstruction is directly proportional to the quality of the stereo matching result. With the gradual development of research in this field, stereo matching is being widely used in 3D reconstruction [ 10 , 11 ], industrial inspection [ 12 , 13 , 14 , 15 ], UAV [ 16 , 17 ], remote sensing [ 18 , 19 ], and intelligent control of robots [ 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Research on the Improvement of Semi-Global Matching Algorithm for Binocular Vision Based on Lunar Surface Environment

Guo

2023

Sensors

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The low light conditions, abundant dust, and rocky terrain on the lunar surface pose challenges for scientific research. To effectively perceive the surrounding environment, lunar rovers are equipped with binocular cameras. In this paper, with the aim of accurately detect obstacles on the lunar surface under complex conditions, an Improved Semi-Global Matching (I-SGM) algorithm for the binocular cameras is proposed. The proposed method first carries out a cost calculation based on the improved Census transform and an adaptive window based on a connected component. Then, cost aggregation is performed using cross-based cost aggregation in the AD-Census algorithm and the initial disparity of the image is calculated via the Winner-Takes-All (WTA) strategy. Finally, disparity optimization is performed using left–right consistency detection and disparity padding. Utilizing standard test image pairs provided by the Middleburry website, the results of the test reveal that the algorithm can effectively improve the matching accuracy of the SGM algorithm, while reducing the running time of the program and enhancing noise immunity. Furthermore, when applying the I-SGM algorithm to the simulated lunar environment, the results show that the I-SGM algorithm is applicable in dim conditions on the lunar surface and can better help a lunar rover to detect obstacles during its travel.

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Binocular Vision Object Positioning Method for Robots Based on Coarse-fine Stereo Matching

Cited by 15 publications

References 8 publications

Automatic Roadblock Identification Algorithm for Unmanned Vehicles Based on Binocular Vision

Automatic Roadblock Identification Algorithm for Unmanned Vehicles Based on Binocular Vision

A Systematic Solution for Moving-Target Detection and Tracking While Only Using a Monocular Camera

Research on the Improvement of Semi-Global Matching Algorithm for Binocular Vision Based on Lunar Surface Environment

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