ICUAS'17 Paper Abstract

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Paper WeC1.4

De Mel, Daniel Henry Sebastion (The University of Auckland), Stol, Karl (University of Auckland), Mills, Jay Alexander Davis (University of Auckland), Eastwood, Blair Richard (University of Auckland)

Vision-Based Object Path Following on a Quadcopter for GPS-Denied Environments

Scheduled for presentation during the "Autonomy - III" (WeC1), Wednesday, June 14, 2017, 17:40−18:00, Salon E

2017 International Conference on Unmanned Aircraft Systems, June 13-16, 2017, Miami Marriott Biscayne Bay, Miami, FL,

This information is tentative and subject to change. Compiled on April 12, 2021

Keywords Autonomy, Control Architectures, Micro- and Mini- UAS

Abstract

This paper presents the development of an object path following algorithm for a quadcopter using on-board vision and localization. This development aims to increase capabilities of autonomous unmanned aerial vehicles (UAV), with the use of object path following. By following an objectís path, reliance on obstacle avoidance strategies can be reduced, under the assumption that the objectís path is free of static obstacles. Relative position of the target has been estimated using on-board processing and monocular vision. On-board localization has been achieved through dead-reckoning, where velocity estimates are calculated from optical flow. Horizontal positional estimates with a standard deviation of 6.5cm have been achieved experimentally with this method. The use of optical flow allows for localization in GPS-denied environments. Both the position and path following can successfully run in moderate winds and variable lighting. The UAV can station-keep to within 40 cm of desired position. The objectís path can be followed with an rms error of 28 cm and maximum deviation of 78 cm.

 

 

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