Correlation Flow: Robust Optical Flow Using Kernel Cross-Correlators

TL;DR

Correlation Flow introduces a kernel cross-correlator based algorithm for monocular optical flow estimation, offering robustness to motion blur and additional velocity parameters, suitable for low-power autonomous drone navigation.

Contribution

It presents a novel correlation flow method using kernel cross-correlators for monocular optical flow, capable of estimating velocity, altitude change, and yaw rate.

Findings

Provides reliable velocity estimation in low-power scenarios

Robust to motion blur in optical flow estimation

Enables altitude velocity and yaw rate estimation

Abstract

Robust velocity and position estimation is crucial for autonomous robot navigation. The optical flow based methods for autonomous navigation have been receiving increasing attentions in tandem with the development of micro unmanned aerial vehicles. This paper proposes a kernel cross-correlator (KCC) based algorithm to determine optical flow using a monocular camera, which is named as correlation flow (CF). Correlation flow is able to provide reliable and accurate velocity estimation and is robust to motion blur. In addition, it can also estimate the altitude velocity and yaw rate, which are not available by traditional methods. Autonomous flight tests on a quadcopter show that correlation flow can provide robust trajectory estimation with very low processing power. The source codes are released based on the ROS framework.