Acoustic sensors play an important role in autonomous underwater vehicles (AUVs). Sidescan sonar (SSS) detects a wide range and provides photo-realistic images in high resolution. However, SSS projects the 3D seafloor to 2D images, which are distorted by the AUV's altitude, target's range and sensor's resolution. As a result, the same physical area can show significant visual differences in SSS images from different survey lines, causing difficulties in tasks such as pixel correspondence and template matching. In this paper, a canonical transformation method consisting of intensity correction and slant range correction is proposed to decrease the above distortion. The intensity correction includes beam pattern correction and incident angle correction using three different Lambertian laws (cos, cos2, cot), whereas the slant range correction removes the nadir zone and projects the position of SSS elements into equally horizontally spaced, view-point independent bins. The proposed method is evaluated on real data collected by a HUGIN AUV, with manually-annotated pixel correspondence as ground truth reference. Experimental results on patch pairs compare similarity measures and keypoint descriptor matching. The results show that the canonical transformation can improve the patch similarity, as well as SIFT descriptor matching accuracy in different images where the same physical area was ensonified.

翻译：声学传感器在自主水下航行器（AUV）中扮演重要角色。侧扫声纳（SSS）探测范围广，可提供高分辨率的光学真实感图像。然而，SSS将三维海底投影到二维图像，导致图像受AUV高度、目标距离和传感器分辨率影响而产生畸变。因此，同一物理区域在不同测线获得的SSS图像中可能呈现显著视觉差异，给像素对应与模板匹配等任务带来困难。本文提出一种由强度校正和斜距校正组成的规范变换方法，以减小上述畸变。强度校正包括波束模式校正和入射角校正，采用三种不同的朗伯定律（cos、cos²、cot）；斜距校正则消除天底区，并将SSS单元的位置投影到等水平间距、与视点无关的区间内。该方法基于HUGIN AUV采集的真实数据进行评估，以人工标注的像素对应关系作为真值参考。实验采用图像块对，比较了相似性度量与关键点描述子匹配效果。结果表明，规范变换能够提升同一物理区域在不同声呐图像中的图像块相似性及SIFT描述子匹配精度。