Global Completion of Occupancy Maps Using Generative Models

Generative AI enables realistic data generation, making it essential for tasks such as text, image, and video synthesis. In robotics, predicting unknown map regions as an image generation problem is key to improving exploration and navigation. This study uniquely compares the performance of three deep generative models—Conditional Variational Autoencoder (CVAE), Vision Transformer (ViT), and Conditional Generative Adversarial Network (CGAN)—for map completion. Unlike prior studies focusing on individual models, this work provides a direct performance comparison for global map prediction. A custom dataset, derived from HouseExpo and enriched with structured time-series exploration data in Gazebo, was developed for this purpose. Experimental results demonstrate that CGAN achieves superior performance in completing unknown regions, making it more effective for robotic exploration. Additionally, we introduce a novel dataset generation methodology leveraging ROS, Gazebo, and Voronoi-based mapping. Future research will focus on scaling the dataset and integrating these models into exploration systems, further advancing generative AI applications in robotics.