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


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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Available from: Cédric Demonceaux
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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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    ABSTRACT: Mostly quadcopter has a flight controller to receive signal from remote control to control four brushless motor speed. In this paper, the researchers introduced a new control method to make quadcopter altitude lock system using Fuzzy-PID and perform a comparative performance analysis between the Fuzzy controller and the new Fuzzy-PID controller. Fuzzy controller has ability to solve uncertainty within the system, by incorporating with altitude sensor data. On the other hand, Fuzzy-PID has the ability to gain the target level with Kp, Ki, Kd values controlled. In this paper the researchers present an analysis to compare the control method between Fuzzy and Fuzzy-PID with regards to the stability altitude lock system. The stability of the altitude lock system can be measured by how small the oscillations occurred. Fuzzy control has shown to produce better result than Fuzzy-PID control. Fuzzy control has 14 cm as its average oscillation, while Fuzzy-PID recorded 24 cm as its average oscillation.
    Preview · Article · Dec 2015
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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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    ABSTRACT: While having the benefit of mechanical simplicity, model-scale unmanned aerial vehicles with only two elevon control surfaces present interesting challenges in dynamics modeling, autopilot design, and field tuning. Because of limited on-board computing and communication bandwidth, traditional control theory was applied to systematically tune the proportional-integral-derivative-based (PID) autopilots offline. Based on the aerodynamic analysis, its multi-input, multi-output underactuated linear model configuration was deduced. Utilizing the real-time flight data collected from human-controlled test flight, a two-input three-output linear model was obtained by means of system identification. It includes the transfer functions in the airspeed loop, heading loop, and altitude loop. The dynamic behavior of the aircraft was analyzed, and five PID controllers in three loops were designed based on the root-locus techniques. The controllers were implemented and further tuned in field flights with improved performances. We demonstrate that with proper precautions, traditional control theory can be used to solve complex control problems that are often tackled with nonlinear control algorithms.
    Full-text · Article · Nov 2015 · IEEE Transactions on Control Systems Technology
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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.
    Full-text · Conference Paper · May 2014
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