Development of a stereo vision measurement architecture for an underwater robot

Sánchez-Ferreira, Camilo; Mori, Jones Y.; Llanos, Carlos H.; Fortaleza, Eugênio

doi:10.1109/lascas.2013.6519001

Cited by 5 publications

(8 citation statements)

References 11 publications

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“…Again, this is based on the same idea of creating a blob of the detected object, and then computing the centroid on each camera. Another feature-based method was implemented in [10] by using background subtraction to segment a moving object, and then determine its center of mass as centroid.…”

Section: Related Workmentioning

confidence: 99%

“…Other works such as [10,11] propose taking several reference points to create a curve of disparity over distance. After obtaining the curve that best fits the experimental data, they are able to create a calibration post-processing function that has to be computed only once per frame.…”

Section: Related Workmentioning

confidence: 99%

“…In stereo vision, the distance from the object to the stereo camera set is computed by obtaining the disparity, which is the difference of the projected points on the two stereo images, and then applying trigonometry [6,10,19,20]. Using a general configuration of a camera set, where the cameras can be in any position with respect to each other, the equation system to solve the depth from the disparity is very complex.…”

Section: Stereo Vision Systemmentioning

confidence: 99%

“…On the other hand, for feature-based systems there are other alternatives because the depth computation is just over a few points on the image. One of the alternatives for overcoming the epipolar constraint is the offline generation of the relationship between disparity and depth proposed by [10,11], for instance. This mechanism generates a curve from several reference points, which relates the disparity to depth.…”

Section: Rectification and Calibrationmentioning

confidence: 99%

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Autonomous stereo vision system for depth computation of moving object

Oviedo¹

2021

Preprint

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This work targets one real world application of stereo vision technology: the computation of the depth information of a moving object in a scene. It uses a stereo camera set that captures the stereoscopic view of the scene. Background subtraction algorithm is used to detect the moving object, supported by the recursive filter of first order as updating method. Mean filter is the pre-processing stage, combined with frame downscaling to reduce the background storage. After thresholding the background subtraction result, the binary image is sent to the software processing unit to compute the centroid of the moving area, and the measured disparity, estimate the disparity by Kalman algorithm, and finally calculate the depth from the estimated disparity. The implementation successfully achieves the objectives of resolution 720p, at 28.68 fps and maximum permissible depth error of ±4 cm (1.066 %) for a depth measuring range from 25 cm to 375 cm.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Stereo Vision Systemmentioning

confidence: 99%

Section: Rectification and Calibrationmentioning

confidence: 99%

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Autonomous stereo vision system for depth computation of moving object

Oviedo¹

2021

Preprint

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“…The pixel coordinates of the imaging points in the left and right cameras are ðu l ; v l Þ and ðu r ; v r Þ, respectively. The perspective transformations of left and right cameras are expressed as equations (18) and (19) According to equations (14) to (16), the real 3-D coordinates of the object can be calculated as equation 21x…”

Section: Underwater Binocular Stereo 3-d Measurementmentioning

confidence: 99%

Accurate binocular stereo underwater measurement method

Tang

2019

International Journal of Advanced Robotic Systems

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Light changes its direction of propagation before entering a camera enclosed in a waterproof housing owing to refraction, which means that perspective imaging models in the air cannot be directly used underwater. In this article, we propose an accurate binocular stereo measurement system in an underwater environment. First, based on the physical underwater imaging model without approximation and Tsai’s calibration method, the proposed system is calibrated to acquire the extrinsic parameters, as the internal parameters can be pre-calibrated in air. Then, based on the calibrated camera parameters, an image correction method is proposed to convert the underwater images to air images. Thus, the epipolar constraint can be used to search the matching point directly. The experimental results show that the proposed method in this article can effectively eliminate the effect of refraction in the binocular vision and the measurement accuracy can be compared with the measurement result in air.

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