Monocular Depth Estimation for Drone Obstacle Avoidance in Indoor Environments

Autonomous nano-quadcopters possess large potential for indoor use. Existing works on autonomous flight however rely on large amounts of compute, therefore resulting in heavy and bulky platforms that can only be safely deployed outdoors. We present a monocular depth estimation method for autonomous indoor obstacle avoidance and waypoint navigation of nano-quadcopters demonstrated on the Bitcraze Crazyflie 2.1 which weighs a mere 33g. Our depth estimation model has 1.56 million parameters and is 4 MB, which after quantization becomes 1 MB. We transmit the images via WiFi from the onboard grayscale camera on the Bitcraze to a laptop, which then runs the 1 MB quantized model to generate small-size depth maps. Subsequently, we run our navigation algorithms on a laptop and transmit high-level motion commands back to the drone. We demonstrate obstacle avoidance capability of this end-to-end system through real-world flights in a variety of indoor environments.