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3D reconstruction of underwater scenes using DIDSON acoustic sonar image sequences through evolutionary algorithms

机译:通过进化算法使用DIDSON声纳图像序列对水下场景进行3D重建

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This paper introduces a methodology to reconstruct underwater environment using two images of the same scene, acquired by an acoustical camera from different points of view. The final target of the work is to produce a full 3D representation of the observed environment to improve its exploration and analysis. Indeed, as the DIDSON acoustic camera provides sequences of 2D images (distance and azimuth), the challenge consists in determining the missing elevation information about the observed scene in order to reconstruct (x, y, z) models, through the computation of the geometrical transformation between the acquisition view points, using image information only. Our research work is divided in two important steps. The first step which is feature point extraction allows robust and shape representative point extraction [1]. The second step presented in this paper uses these specific points appearing on two images and paired accordingly, to determine camera motion (rotation and translation) between the two acquisitions, and points missing elevation in order to reconstruct the observed scene. Due to the problem high-dimensional search space (6 camera motion parameters plus one elevation per pair of points), we propose to achieve the search using CMA-ES optimization algorithm. This stereovision-like optimization procedure assumes a known camera model. The first topic in this paper tries to check the good behavior of the supposed camera model in order to be sure that extracted points from images are robust enough and not affected by extra camera distortions. A set of DIDSON images have been acquired in the Laval University pool and used to perform such a verification, with various objects (wooden boxes and grid) observed from different points of view. Finally, using extracted pairs of points coming from two images, the proposed algorithm is able to retrieve the local relative geometry of the observed scene through the estimation of the missing elevations.
机译:本文介绍了一种使用声学摄像机从不同角度获取的同一场景的两个图像重建水下环境的方法。这项工作的最终目标是对观察到的环境进行完整的3D表示,以改善其探索和分析能力。的确,由于DIDSON声学相机提供2D图像序列(距离和方位角),因此挑战在于确定有关观察场景的缺失高程信息,以便通过计算几何来重建(x,y,z)模型。仅使用图像信息在获取视点之间进行转换。我们的研究工作分为两个重要步骤。第一步是特征点提取,可以进行鲁棒性和形状代表点提取[1]。本文介绍的第二步使用这些出现在两幅图像上的特定点进行相应的配对,以确定两次采集之间的相机运动(旋转和平移),以及缺少高程的点,以重建观察到的场景。由于存在高维搜索空间(6个摄像机运动参数加上每对点一个高程)的问题,我们建议使用CMA-ES优化算法来实现搜索。这种类似于立体视觉的优化过程假设使用已知的相机模型。本文的第一个主题试图检查假设的相机模型的良好行为,以确保从图像中提取的点足够鲁棒,并且不受相机额外失真的影响。一组DIDSON图像已在拉瓦尔大学的游泳池中获取,并用于执行这种验证,并从不同的角度观察了各种物体(木箱和网格)。最后,使用从两幅图像中提取的成对点,提出的算法能够通过估计缺失的高程来检索被观察场景的局部相对几何形状。

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