Tianxiang Lin, Akshay Hinduja, Mohamad Qadri, Michael Kaess

ICRA 2023

Abstract

Underwater robots typically rely on acoustic sensors like sonar to perceive their surroundings. However, these sensors are often inundated with multiple sources and types of noise, which makes using raw data for any meaningful inference with features, objects, or boundary returns very difficult. While several conventional methods of dealing with noise exist, their success rates are unsatisfactory. This paper presents a novel application of conditional Generative Adversarial Networks (cGANs) to train a model to produce noise-free sonar images, outperforming several conventional filtering methods. Estimating free space is crucial for autonomous robots performing active exploration and mapping. Thus, we apply our approach to the task of underwater occupancy mapping and show superior free and occupied space inference when compared to conventional methods.

Citation

T. Lin, A. Hinduja, M. Qadri, and M. Kaess, “Conditional GANs for sonar
image filtering with applications to underwater occupancy mapping,” in
Proc. IEEE Intl. Conf. on Robotics and Automation (ICRA), London, UK,
May 2023.

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