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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.Finally, a PID controller is designed, and the performance of the entire system is tested experimentally.

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

Inclined model of the MFB.
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sensors-15-13270-f015: Inclined model of the MFB.

Mentions: The schematic of the inclined image plane is shown in Figure 15. The relative error caused by the inclined angle to the measurement result can be expressed as: (11)δ=((tan(arctan(l/h)+Δα))+(tan(arctan(l/h)−Δα)))−2l/h2l/h×100% where is the distance of the feature to the center on the ground, is the height of the MFB, is the inclined angle and is the relative error. Plugging in the conventional value , then we can get the relationship of the relative error and the inclined angle , as shown in Figure 16.


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

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

Inclined model of the MFB.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-13270-f015: Inclined model of the MFB.
Mentions: The schematic of the inclined image plane is shown in Figure 15. The relative error caused by the inclined angle to the measurement result can be expressed as: (11)δ=((tan(arctan(l/h)+Δα))+(tan(arctan(l/h)−Δα)))−2l/h2l/h×100% where is the distance of the feature to the center on the ground, is the height of the MFB, is the inclined angle and is the relative error. Plugging in the conventional value , then we can get the relationship of the relative error and the inclined angle , as shown in Figure 16.

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.Finally, a PID controller is designed, and the performance of the entire system is tested experimentally.

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