Correlation flow : robust optical flow using kernel cross-correlators

Correlation flow : robust optical flow using kernel cross-correlators

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 (K...

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Bibliographic Details
Main Authors: Wang, Chen, Ji, Tete, Nguyen, Thien-Minh, Xie, Lihua
Other Authors: School of Electrical and Electronic Engineering
Format: Conference or Workshop Item
Language:English
Published: 2020
Subjects:
Engineering::Computer science and engineering
Kernel
Optical Sensors
Online Access:https://hdl.handle.net/10356/143629
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Institution: Nanyang Technological University
Language: English
Description
Summary: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.

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