Autonomous driving paper index
Implementation of an Efficient and Optical-Flow-Based Algorithm of Depth Estimation on Autonomous Nano Quadcopters for Obstacle Avoidance
One-line summary
In this work, we present FlowDep, an efficient and optical flowbased algorithm for depth estimation.
Engineering notes
Key topics: autonomous driving, depth estimation, monocular camera, deployment. 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 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.
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