Data Driven Strategies for Active Monocular SLAM using Inverse Reinforcement Learning

Learning a complex task like robot maneuver while preventing Monocular SLAM failure is challenging for both robots and humans. We devise a computational model for representing and inferring strategies for this task, formulated as a Markov Decision Process (MDP). We show how the reward function can be learned using Inverse Reinforcement Learning. The resulting framework allows us to understand how chosen parameters affect the quality of Monocular SLAM. A significant improvement in performance as compared to other state-of-the-art methods is also shown.

Authors

• Vignesh Prasad
• Rishabh Jangir Indian Institute of Technology
• Ravindran Balaraman
• K. Madhava Krishna International Institute of Information Technology

Keywords

• Active Monocular SLAM
• Inverse Reinforcement Learning