Place recognition using SOund Navigation and Ranging (SONAR) images is an important task for simultaneous localization and mapping(SLAM) in underwater environments. This paper proposes a robust and efficient imaging SONAR based place recognition, SONAR context, and loop closure method. Unlike previous methods, our approach encodes geometric information based on the characteristics of raw SONAR measurements without prior knowledge or training. We also design a hierarchical searching procedure for fast retrieval of candidate SONAR frames and apply adaptive shifting and padding to achieve robust matching on rotation and translation changes. In addition, we can derive the initial pose through adaptive shifting and apply it to the iterative closest point (ICP) based loop closure factor. We evaluate the performance of SONAR context in the various underwater sequences such as simulated open water, real water tank, and real underwater environments. The proposed approach shows the robustness and improvements of place recognition on various datasets and evaluation metrics. Supplementary materials are available at https://github.com/sparolab/sonar_context.git.

翻译：基于声波导航与测距（SONAR）图像的位置识别是水下环境同时定位与地图构建（SLAM）中的一项重要任务。本文提出了一种鲁棒且高效的成像声呐位置识别方法——声呐上下文（SONAR context）与闭环检测方法。与以往方法不同，本文方法基于原始声呐测量的特征编码几何信息，无需先验知识或训练。我们设计了一种层次化搜索流程，用于快速检索候选声呐帧，并采用自适应偏移与填充技术，实现对旋转与平移变化的鲁棒匹配。此外，通过自适应偏移可推导初始位姿，并将其应用于基于迭代最近点（ICP）的闭环因子。我们在多种水下序列（如仿真开阔水域、真实水槽及真实水下环境）中评估了声呐上下文方法的性能。实验结果表明，所提方法在多种数据集与评价指标上均展现出位置识别的鲁棒性与改进效果。补充材料详见 https://github.com/sparolab/sonar_context.git。