Combining optimal control and learning for autonomous aerial navigation in novel indoor environments
This work addresses the problem of enabling drones to navigate safely in unfamiliar indoor settings, which is incremental as it builds on existing simulation and learning techniques.
The authors tackled autonomous navigation for Micro Aerial Vehicles in novel indoor environments by combining optimal control and learning, achieving obstacle avoidance using only on-board sensor data as demonstrated in the iGibson simulation environment.
This report proposes a combined optimal control and perception framework for Micro Aerial Vehicle (MAV) autonomous navigation in novel indoor enclosed environments, relying exclusively on on-board sensor data. We use privileged information from a simulator to generate optimal waypoints in 3D space that our perception system learns to imitate. The trained learning based perception module in turn is able to generate similar obstacle avoiding waypoints from sensor data (RGB + IMU) alone. We demonstrate the efficacy of the framework across novel scenes in the iGibson simulation environment.