  Side-scan sonar (SSS) is a lightweight acoustic sensor that is commonly
deployed on autonomous underwater vehicles (AUVs) to provide high-resolution
seafloor images. However, leveraging side-scan images for simultaneous
localization and mapping (SLAM) presents a notable challenge, primarily due to
the difficulty of establishing sufficient amount of accurate correspondences
between these images. To address this, we introduce a novel subframe-based
dense SLAM framework utilizing side-scan sonar data, enabling effective dense
matching in overlapping regions of paired side-scan images. With each image
being evenly divided into subframes, we propose a robust estimation pipeline to
estimate the relative pose between each paired subframes, by using a good
inlier set identified from dense correspondences. These relative poses are then
integrated as edge constraints in a factor graph to optimize the AUV pose
trajectory.
  The proposed framework is evaluated on three real datasets collected by a
Hugin AUV. Among one of them includes manually-annotated keypoint
correspondences as ground truth and is used for evaluation of pose trajectory.
We also present a feasible way of evaluating mapping quality against multi-beam
echosounder (MBES) data without the influence of pose. Experimental results
demonstrate that our approach effectively mitigates drift from the
dead-reckoning (DR) system and enables quasi-dense bathymetry reconstruction.
An open-source implementation of this work is available.
