# Conditional GANs for Sonar Image Filtering with Applications to Underwater Occupancy Mapping

arXiv: https://arxiv.org/abs/2209.11757 · HTML: https://mlsanity.com/paper/2209.11757/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2022.

## Built on by (most cited first, 4 of 4)

- [Z-Splat: Z-Axis Gaussian Splatting for Camera-Sonar Fusion](https://mlsanity.com/paper/2404.04687/) (2024)
- [AONeuS: A Neural Rendering Framework for Acoustic-Optical Sensor Fusion](https://mlsanity.com/paper/2402.03309/) (2024)
- [Acoustic Neural 3D Reconstruction Under Pose Drift](https://mlsanity.com/paper/2503.08930/) (2025)
- [Adapting Vision Foundation Models to Acoustics for Pose-Free 3D Sonar Reconstruction](https://mlsanity.com/paper/2609.06261/) (2026)

## Abstract (arXiv)

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.