Monocular Depth Estimation for Drone Obstacle Avoidance in Indoor Environments

Engineering notes

Key topics: autonomous driving, end-to-end, depth estimation, monocular depth. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

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.

Links and sources

• Official / arXiv page

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