  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.
