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.

本文提出了一种基于强度和斜距离纠正的规范化变换方法，该方法对由侧扫声纳所采集的图像数据中的失真和相位差进行了修正，并成功地在真实环境中进行了实验验证。