Xu, Shida; Zhang, Kaicheng; Hong, Ziyang; Liu, Yuanchang; Wang, Sen; (2024) DISO: Direct Imaging Sonar Odometry. In: 2024 IEEE International Conference on Robotics and Automation (ICRA). (pp. pp. 8573-8579). IEEE: Yokohama, Japan.

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Abstract

This paper introduces a novel sonar odometry system that estimates the relative spatial transformation between two sonar image frames. Considering the unique challenges, such as low resolution and high noise, of sonar imagery for odometry and Simultaneous Localization and Mapping (SLAM), the proposed Direct Imaging Sonar Odometry (DISO) system is designed to estimate the relative transformation between two sonar frames by minimizing the aggregated sonar intensity errors of points with high intensity gradients. Moreover, DISO is implemented to incorporate a multi-sensor window optimization technique, a data association strategy and an acoustic intensity outlier rejection algorithm for reliability and accuracy. The effectiveness of DISO is evaluated using both simulated and real-world sonar datasets, showing that it outperforms the existing geometric-only method on localization accuracy and achieves state-of-the-art sonar odometry performance. We release the source codes of the DISO implementation to the community. The source code is available at https://github.com/SenseRoboticsLab/DISO.

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