Optical-Flow-Based Algorithm of Depth Estimation with Model-Free Control Policy on Autonomous Nano Quadcopters for Obstacle Avoidance

Nano quadcopters are small, agile, and cost-effective Internet of Things platforms, especially appropriate for narrow and cluttered environments. We developed a model-free control policy combined with FlowDep, an efficient optical flow depth estimation algorithm that computes object depth information using vision. FlowDep was successfully deployed on the Bitcraze Crazyflie 2.1 (with weight ~34 g) using its monocular camera for obstacle avoidance. FlowDep calculated depth information from images and use multizone scheme for control policy. Successful obstacle avoidance is demonstrated. The developed policy showed its potential for future applications in complex environment exploration to enhance the autonomous flight and perception abilities of drones.