Implementation of an Efficient and Optical-Flow-Based Algorithm of Depth Estimation on Autonomous Nano Quadcopters for Obstacle Avoidance

Nano quadcopters are small, agile, and cheap platforms well suited for deployment in narrow, cluttered environments. Due to their limited payload, these vehicles are highly constrained in computational power, making conventional vision-based navigation methods impractical for implementation. In this work, we present FlowDep, an efficient and optical flowbased algorithm for depth estimation. We draw inspiration from the low-resolution but efficient motion-detection mechanisms in insects. We successfully demonstrate the capabilities of the FlowDep by deploying it on a Bitcraze Crazyflie, a ~30 g nano quadcopter for obstacle avoidance with a single monocular camera. Additionally, we demonstrate the feasibility of the FlowDep algorithm in Gazebo simulation for obstacle avoidance in indoor and outdoor test environments.