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Monocular-Vision-Based Autonomous Hovering for a Miniature Flying Ball.

Lin J, Han B, Luo Q - Sensors (Basel) (2015)

Bottom Line: The position variations between the subsequent frames and the reference image are calculated by comparing their correspondence points.The Kalman filter is used to predict the position of the miniature flying ball to handle situations, such as a lost or wrong frame.The results show that the proposed method can keep the aircraft in a stable hover.

View Article: PubMed Central - PubMed

Affiliation: School of Mechanical Engineering, Beijing Institute of Technology, 5 Zhongguancun South Street, Haidian District, Beijing 100081, China. linjunqin2010@163.com.

ABSTRACT
This paper presents a method for detecting and controlling the autonomous hovering of a miniature flying ball (MFB) based on monocular vision. A camera is employed to estimate the three-dimensional position of the vehicle relative to the ground without auxiliary sensors, such as inertial measurement units (IMUs). An image of the ground captured by the camera mounted directly under the miniature flying ball is set as a reference. The position variations between the subsequent frames and the reference image are calculated by comparing their correspondence points. The Kalman filter is used to predict the position of the miniature flying ball to handle situations, such as a lost or wrong frame. Finally, a PID controller is designed, and the performance of the entire system is tested experimentally. The results show that the proposed method can keep the aircraft in a stable hover.

No MeSH data available.


Related in: MedlinePlus

Software architecture.
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sensors-15-13270-f007: Software architecture.

Mentions: The main software on the ground station consists of two major parts: vision-based position estimation and a controller. Besides, there are two minor blocks in the entire software structure: the GUI block and the communication block. As shown in Figure 7, the GUI block can provide the interface between the entire system and the user for sensory data displaying, parameter controlling and parameter tuning. The communication block is used to integrate the hardware and software between the aircraft and the ground station.


Monocular-Vision-Based Autonomous Hovering for a Miniature Flying Ball.

Lin J, Han B, Luo Q - Sensors (Basel) (2015)

Software architecture.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507670&req=5

sensors-15-13270-f007: Software architecture.
Mentions: The main software on the ground station consists of two major parts: vision-based position estimation and a controller. Besides, there are two minor blocks in the entire software structure: the GUI block and the communication block. As shown in Figure 7, the GUI block can provide the interface between the entire system and the user for sensory data displaying, parameter controlling and parameter tuning. The communication block is used to integrate the hardware and software between the aircraft and the ground station.

Bottom Line: The position variations between the subsequent frames and the reference image are calculated by comparing their correspondence points.The Kalman filter is used to predict the position of the miniature flying ball to handle situations, such as a lost or wrong frame.The results show that the proposed method can keep the aircraft in a stable hover.

View Article: PubMed Central - PubMed

Affiliation: School of Mechanical Engineering, Beijing Institute of Technology, 5 Zhongguancun South Street, Haidian District, Beijing 100081, China. linjunqin2010@163.com.

ABSTRACT
This paper presents a method for detecting and controlling the autonomous hovering of a miniature flying ball (MFB) based on monocular vision. A camera is employed to estimate the three-dimensional position of the vehicle relative to the ground without auxiliary sensors, such as inertial measurement units (IMUs). An image of the ground captured by the camera mounted directly under the miniature flying ball is set as a reference. The position variations between the subsequent frames and the reference image are calculated by comparing their correspondence points. The Kalman filter is used to predict the position of the miniature flying ball to handle situations, such as a lost or wrong frame. Finally, a PID controller is designed, and the performance of the entire system is tested experimentally. The results show that the proposed method can keep the aircraft in a stable hover.

No MeSH data available.


Related in: MedlinePlus