Conditional GANs for Sonar Image Filtering with Applications to Underwater Occupancy Mapping
2023 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, 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.
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Key words
acoustic sensors,active exploration,autonomous robots,boundary returns,conditional GANs,conditional Generative Adversarial Networks,conventional filtering methods,free space,meaningful inference,noise-free sonar images,occupied space inference,raw data,sonar image filtering,success rates,underwater occupancy mapping,underwater robots
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