SeGuE: Semantic Guided Exploration for Mobile Robots

The rise of embodied AI applications has enabled robots to perform complex tasks that require sophisticated understanding of their environment. To allow successful robot operation in such settings, maps must be constructed to include both semantic and geometric information. In this paper, we address the novel problem of semantic exploration, whereby a mobile robot must autonomously explore an environment to fully map its structure and features’ semantic appearance. We develop a method based on next-best-view exploration, where potential poses are scored based on the semantic features visible from that pose. We explore two alternative methods for sampling potential views and demonstrate the effectiveness of our framework in both simulation and physical experiments. The automatic creation of high-quality semantic maps can enable robots to better understand and interact with their environments and facilitate the deployment of future embodied AI applications.