EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots

TL;DR

EdgeFlowNet is a high-speed, energy-efficient dense optical flow method designed for tiny mobile robots, enabling real-time navigation and obstacle avoidance with significant improvements over previous approaches.

Contribution

We introduce EdgeFlowNet, a novel optical flow approach optimized for tiny robots, achieving 20x speed, over 20% accuracy improvement, and low power consumption suitable for onboard deployment.

Findings

20x faster than previous methods

Over 20% accuracy improvement

Operates at 1.08W power consumption

Abstract

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.