EdgeFlowNet让微型无人机每秒处理100帧光流，功耗仅1.08瓦。
EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots
- 利用边缘计算实现高速低延迟光流估计
- 比现有最优方法快20倍，精度提升超20%
- 适合功耗敏感的微型机器人自主导航
光流估计对微型移动机器人实现安全导航、避障等功能至关重要，但受限于机载感知与计算能力，实现难度大。本文提出EdgeFlowNet，一种面向微型自主机器人的高速低延迟密集光流方法，充分利用边缘计算能力。通过在微型四轴飞行器上部署EdgeFlowNet，实现了静态障碍物避让、未知间隙穿越及动态障碍物躲避。该方法相较此前最先进方案提速约20倍，精度提升超过20%，且仅消耗1.08瓦功率，使掌上大小的微型机器人具备高级自主能力。
原文摘要 · Abstract (English)
Optical flow estimation is a critical task for tiny mobile robotics to enable safe and accurate navigation, obstacle avoidance, and other functionalities. However, optical flow estimation on tiny robots is challenging due to limited onboard sensing and computation capabilities. In this paper, we propose EdgeFlowNet , a high-speed, low-latency dense optical flow approach for tiny autonomous mobile robots by harnessing the power of edge computing. We demonstrate the efficacy of our approach by deploying EdgeFlowNet on a tiny quadrotor to perform static obstacle avoidance, flight through unknown gaps and dynamic obstacle dodging. EdgeFlowNet is about 20 faster than the previous state-of-the-art approaches while improving accuracy by over 20% and using only 1.08W of power enabling advanced autonomy on palm-sized tiny mobile robots.
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