Underwater sensing with omni-directional stereo camera

Yamashita, Atsushi; Kawanishi, Ryosuke; Koketsu, Tadashi; Kaneko, Toru; Asama, Hajime

doi:10.1109/iccvw.2011.6130257

Cited by 10 publications

(2 citation statements)

References 31 publications

(29 reference statements)

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“…Thus, the large FOV of the fish-eye lens had not been fully utilized, and the distortion rectification introduced a significant error. Yamashita et al 11 studied a stereo matching algorithm for underwater panoramic images acquired through a convex mirror and a general lens rather than using a fish-eye lens. The vision system was complex, and the captured images were also rectified before the stereo matching.…”

Section: Introductionmentioning

confidence: 99%

Epipolar geometry and stereo matching algorithm for underwater fish-eye images

Zhang

et al. 2018

International Journal of Advanced Robotic Systems

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A fish-eye lens achieves a large field of view at the cost of image distortion, and underwater imaging by a fish-eye lens introduces refraction when light passes through the different medium. To obtain three-dimensional information, under these circumstances, a stereo matching algorithm is proposed that analyzes the geometric characteristics of an underwater fish-eye image, taking into account distortion and refraction. First, the underwater imaging model of a stereo fisheye camera is established, and the epipolar curve of the underwater fish-eye image is calculated. Then, in the matching step, an adaptive window based on mean-shift segmentation is proposed to further alleviate the impact of distortion. Experiments are performed on synthetic images and natural scene images. The results show that the proposed model and the calculated epipolar curve are effective and that the adaptive window method could improve the precision of stereo matching on underwater fish-eye images.

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Section: Introductionmentioning

confidence: 99%

Epipolar geometry and stereo matching algorithm for underwater fish-eye images

Zhang

et al. 2018

International Journal of Advanced Robotic Systems

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“…Regarding underwater cameras, very few works on omnidirectional underwater cameras can be found. Yamashita [ 17 ] proposed an omnidirectional underwater stereo sensor based on individual conventional video cameras and hyperboloid mirrors inside an acrylic cylindrical waterproof case. As mentioned before, this solution has not been adopted, as the use of an OMS allows one to capture panoramas in higher resolution and is more uniformly distributed.…”

Section: Introductionmentioning

confidence: 99%

Omnidirectional Underwater Camera Design and Calibration

Bosch

Gracias

Ridao

et al. 2015

Sensors

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This paper presents the development of an underwater omnidirectional multi-camera system (OMS) based on a commercially available six-camera system, originally designed for land applications. A full calibration method is presented for the estimation of both the intrinsic and extrinsic parameters, which is able to cope with wide-angle lenses and non-overlapping cameras simultaneously. This method is valid for any OMS in both land or water applications. For underwater use, a customized housing is required, which often leads to strong image distortion due to refraction among the different media. This phenomena makes the basic pinhole camera model invalid for underwater cameras, especially when using wide-angle lenses, and requires the explicit modeling of the individual optical rays. To address this problem, a ray tracing approach has been adopted to create a field-of-view (FOV) simulator for underwater cameras. The simulator allows for the testing of different housing geometries and optics for the cameras to ensure a complete hemisphere coverage in underwater operation. This paper describes the design and testing of a compact custom housing for a commercial off-the-shelf OMS camera (Ladybug 3) and presents the first results of its use. A proposed three-stage calibration process allows for the estimation of all of the relevant camera parameters. Experimental results are presented, which illustrate the performance of the calibration method and validate the approach.

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