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On 3-D Motion Estimation From Feature Tracks in 2-D FS Sonar Video

机译:基于2-D FS声纳视频中特征轨迹的3-D运动估计

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摘要

Visual odometry involves the computation of 3-D motion and (or) trajectory by tracking features in the video or image sequences recorded by the camera(s) on some autonomous terrestrial, aerial, and marine robotics platform. For exploration, mapping, inspection, and surveillance operations within turbid waters, high-frequency 2-D forward-scan sonar systems offer a significant advantage over cameras by providing both imagery with target details and attractive tradeoff in range, resolution, and data rate. Operating these at grazing incidence gives larger scene coverage and improved image quality due to the dominance of diffuse backscattered reflectance but induces cast shadows that are typically more distinct than brightness patterns due to the direct reflectance of casting objects. For the computation of 3-D motion by automatic video processing, the estimation accuracy and robustness can be enhanced by integrating the visual cues from shadow dynamics with the image flow of stationary 3-D objects, both induced by sonar motion. In this paper, we present the mathematical models of image flow for 3-D objects and their cast shadows, utilize them in devising various 3-D sonar motion estimation solutions, and study their robustness. We present results of experiments with both synthetic and real data in order to assess the accuracy and performance of these methods.
机译:视觉里程计涉及通过在一些自主的地面,空中和海洋机器人平台上跟踪由摄像机记录的视频或图像序列中的特征来计算3D运动和(或)轨迹。对于在浑浊水域中的勘探,制图,检查和监视操作,高频2D前向扫描声纳系统通过提供具有目标细节的图像以及范围,分辨率和数据速率方面的有吸引力的折衷,提供了优于摄像机的显着优势。由于占主导地位的散射背向散射反射率,在掠入射时进行操作可提供更大的场景覆盖范围并改善图像质量,但由于投射对象的直接反射性,所产生的投射阴影通常比亮度模式更鲜明。对于通过自动视频处理进行的3D运动计算,可以通过将来自阴影动力学的视觉提示与固定的3D对象的图像流(由声纳运动引起)集成在一起,从而提高估计的准确性和鲁棒性。在本文中,我们介绍了3-D对象及其投射阴影的图像流的数学模型,并将其用于设计各种3-D声纳运动估计解决方案,并研究了它们的鲁棒性。我们提出了合成和真实数据的实验结果,以评估这些方法的准确性和性能。

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