一种基于密集子帧并带有侧扫声纳的SLAM框架
A Dense Subframe-based SLAM Framework with Side-scan Sonar
AI总结：
针对侧扫声纳图像难以建立准确对应关系的挑战，提出一种基于密集子帧的SLAM框架，通过子帧密集匹配与因子图优化有效减轻水下航位推算漂移并实现准密集测深重建。
摘要：
侧扫声纳（SSS）是一种轻便的声学传感器，通常部署在自主水下航行器（AUV）上以提供高分辨率海底图像。然而，利用侧扫图像进行同步定位与建图（SLAM）面临显著挑战，主要原因是难以在这些图像之间建立足够数量的准确对应关系。为了解决这个问题，我们引入了一种利用侧扫声纳数据的基于子帧的新型密集SLAM框架，能够在成对侧扫图像的重叠区域进行有效的密集匹配。通过将每幅图像均匀划分为子帧，我们提出了一种鲁棒的估计流水线，利用从密集对应关系中识别出的良好内点集来估计每对子帧之间的相对位姿。这些相对位姿随后作为边约束整合到因子图中，以优化AUV的位姿轨迹。所提出的框架在由Hugin AUV收集的三个真实数据集上进行了评估。其中一个数据集包含手动标注的关键点对应关系作为真值，用于评估位姿轨迹。我们还提出了一种在不考虑位姿影响的情况下，利用多波束测深仪（MBES）数据评估建图质量的可行方法。实验结果表明，我们的方法有效减轻了航位推算（DR）系统的漂移，并实现了准密集的测深重建。本工作的开源实现已提供。
英文摘要：
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.
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