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Nano-size drones hold enormous potential to explore unknown and complexenvironments. Their small size makes them agile and safe for operation close tohumans and allows them to navigate through narrow spaces. However, their tinysize and payload restrict the possibilities for on-board computation andsensing, making fully autonomous flight extremely challenging. The first steptowards full autonomy is reliable obstacle avoidance, which has proven to betechnically challenging by itself in a generic indoor environment. Currentapproaches utilize vision-based or 1-dimensional sensors to support nano-droneperception algorithms. This work presents a lightweight obstacle avoidancesystem based on a novel millimeter form factor 64 pixels multi-zoneTime-of-Flight (ToF) sensor and a generalized model-free control policy.Reported in-field tests are based on the Crazyflie 2.1, extended by a custommulti-zone ToF deck, featuring a total flight mass of 35g. The algorithm onlyuses 0.3% of the on-board processing power (210uS execution time) with a framerate of 15fps, providing an excellent foundation for many future applications.Less than 10% of the total drone power is needed to operate the proposedperception system, including both lifting and operating the sensor. Thepresented autonomous nano-size drone reaches 100% reliability at 0.5m/s in ageneric and previously unexplored indoor environment. The proposed system isreleased open-source with an extensive dataset including ToF and gray-scalecamera data, coupled with UAV position ground truth from motion capture.

Robust and Efficient Depth-based Obstacle Avoidance for Autonomous Miniaturized UAVs