Autonomous driving paper index
Optical-Flow-Based Algorithm of Depth Estimation with Model-Free Control Policy on Autonomous Nano Quadcopters for Obstacle Avoidance
One-line summary
We developed a model-free control policy combined with FlowDep, an efficient optical flow depth estimation algorithm that computes object depth information using vision.
Engineering notes
Key topics: autonomous driving, depth estimation, monocular camera, perception, control. 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
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.
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