Vision-based pose estimation of a multi-rotor unmanned aerial vehicle. Issue 3 (1st July 2019)

• Record Type: Journal Article
• Authors: Gupta, Kashish; Emran, Bara Jamal; Najjaran, Homayoun
• Is Part Of: International journal of intelligent unmanned systems. Volume 7:Issue 3(2019)
• Page Start: 120
• Page End: 132
• Publication Date: 2019-07-01
• Subjects: Unmanned aerial vehicles -- Computer vision -- 3-D pose estimation -- Autonomous landing; Vehicles, Remotely piloted -- Periodicals; Robots -- Control systems -- Periodicals; Mechanical engineering -- Robots -- Periodicals; Robotics -- Periodicals; Submersibles -- Periodicals; Space vehicles -- Command control systems -- Periodicals; 629.046
• DOI: 10.1108/IJIUS-10-2018-0030
• Languages: English
• ISSNs: 2049-6427

Abstract

Abstract : Purpose: The purpose of this paper is to facilitate autonomous landing of a multi-rotor unmanned aerial vehicle (UAV) on a moving/tilting platform using a robust vision-based approach. Design/methodology/approach: Autonomous landing of a multi-rotor UAV on a moving or tilting platform of unknown orientation in a GPS-denied and vision-compromised environment presents a challenge to common autopilot systems. The paper proposes a robust visual data processing system based on targets' Oriented FAST and Rotated BRIEF features to estimate the UAV's three-dimensional pose in real time. Findings: The system is able to visually locate and identify the unique landing platform based on a cooperative marker with an error rate of 1° or less for all roll, pitch and yaw angles. Practical implications: The proposed vision-based system aims at on-board use and increased reliability without a significant change to the computational load of the UAV. Originality/value: The simplicity of the training procedure gives the process the flexibility needed to use a marker of any unknown/irregular shape or dimension. The process can be easily tweaked to respond to different cooperative markers. The on-board computationally inexpensive process can be added to off-the-shelf autopilots.