From Monocular SLAM to Autonomous Drone Exploration
This work addresses autonomous exploration for drones with limited payload and power, though it is incremental as it builds on existing SLAM techniques.
The paper tackled autonomous navigation for micro aerial vehicles using a low-cost monocular camera, proposing a method that adapts LSD-SLAM for obstacle mapping and exploration in texture-less areas, and demonstrated it on a Parrot Bebop MAV in experiments.
Micro aerial vehicles (MAVs) are strongly limited in their payload and power capacity. In order to implement autonomous navigation, algorithms are therefore desirable that use sensory equipment that is as small, low-weight, and low-power consuming as possible. In this paper, we propose a method for autonomous MAV navigation and exploration using a low-cost consumer-grade quadrocopter equipped with a monocular camera. Our vision-based navigation system builds on LSD-SLAM which estimates the MAV trajectory and a semi-dense reconstruction of the environment in real-time. Since LSD-SLAM only determines depth at high gradient pixels, texture-less areas are not directly observed so that previous exploration methods that assume dense map information cannot directly be applied. We propose an obstacle mapping and exploration approach that takes the properties of our semi-dense monocular SLAM system into account. In experiments, we demonstrate our vision-based autonomous navigation and exploration system with a Parrot Bebop MAV.