CVI-SLAM—Collaborative Visual-Inertial SLAM

Abstract

With robotic perception constituting the biggest impediment before robots are ready for employment in real missions, the promise of more efficient and robust robotic perception in multiagent, collaborative missions can have a great impact on many robotic applications. Employing an ubiquitous and well-established visual-inertial setup onboard each agent, in this letter, we propose CVI-SLAM, a novel visual-inertial framework for centralized collaborative simultaneous localization and mapping (SLAM). Sharing all information with a central server, each agent outsources computationally expensive tasks, such as global map optimization to relieve onboard resources and passes on measurements to other participating agents, while running visual-inertial odometry onboard to ensure autonomy throughout the mission. Thoroughly analyzing CVI-SLAM, we attest to its accuracy and the improvements arising from the collaboration, and evaluate its scalability in the number of participating agents and applicability in terms of network requirements.