Article

Vision Based UAV Attitude Estimation: Progress and Insights

Journal of Intelligent and Robotic Systems (Impact Factor: 1.18). 01/2012; 65(1-4):295-308. DOI: 10.1007/s10846-011-9588-y
Source: DBLP

ABSTRACT

Unmanned aerial vehicles (UAVs) are increasingly replacing manned systems in situations that are dangerous, remote, or difficult
for manned aircraft to access. Its control tasks are empowered by computer vision technology. Visual sensors are robustly
used for stabilization as primary or at least secondary sensors. Hence, UAV stabilization by attitude estimation from visual
sensors is a very active research area. Vision based techniques are proving their effectiveness and robustness in handling
this problem. In this work a comprehensive review of UAV vision based attitude estimation approaches is covered, starting
from horizon based methods and passing by vanishing points, optical flow, and stereoscopic based techniques. A novel segmentation
approach for UAV attitude estimation based on polarization is proposed. Our future insightes for attitude estimation from
uncalibrated catadioptric sensors are also discussed.

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    • "Studies on quadcopter modeling and control had increased rapidly in recent years. Examples of some studies are as follows: developments of flying robots includeing dynamic modeling, vehicle design optimization and control, new controller to improve the ability to control the orientation angles [6], low cost development of an autonomous hover for quadcopter [10], design and control of quadrotor prototype with 3-axis accelerometer and compass as its sensors, introduction of the Kalman filter, sensors and motors dynamics in the control loop [11], a simpler method for segmentation and horizon detection based on polarization, the catadioptric sensors used, and a comprehensive review on attitude estimation approaches from visual sensors [12]. In the development of hybrid controller, the researchers believed that the control performance of the Fuzzy PD controller was slightly better then the classical PD controller in simulations and experiments, as the biggest advantage of the hybrid fuzzy PD controller is the robustness against noise, and its ease for implementation [13]. "
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    • "While a study on aerodynamic technologies can be found in [6], an overview of flight control systems of small UAVs was presented in [7]. Furthermore, in [8] and [9], UAV control based on computer vision was proposed, while in [10], a kinematic model-based design was reported. Modelling and adaptive control was demonstrated in [11]. "
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    • "Such is the case of vision-aided estimation using on board cameras, either fish eye [14], perspective[15] or even both [16]. Shabayek et al. recently published a survey of vision aided estimation methods [14]. When outdoor, it is also possible to use complementary GPS information [10], [17], or other sensors such as Doppler and Laser Radar [18], whereas indoor solutions may include laser range finding capabilities coupled with Simultaneous Location And Mapping (SLAM) algorithms [19]. "
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    ABSTRACT: This paper introduces a novel algorithm to obtain attitude estimations from low cost 9 Degree of Freedom Inertial Measurement Units. This nonlinear attitude estimator is formulated in the Special Orthogonal Group SO(3) based on the Lya-punov theory. The performance of the proposed estimator is compared to current commonly used methods, namely the Extended Kalman Filter and two other nonlinear estimators in SO(3), in computer simulations for a quadrotor Unmanned Aerial Vehicle.
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