Unmanned aerial vehicles UAVs attitude, height, motion estimation and control using visual systems

Abstract

This paper presents an implementation of an aircraft pose and motion estimator using visual systems as the principal sensor for controlling an Unmanned Aerial Vehicle (UAV) or as a redundant system for an Inertial Measure Unit (IMU) and gyros sensors. First, we explore the applications of the unified theory for central catadioptric cameras for attitude and heading estimation, explaining how the skyline is projected on the catadioptric image and how it is segmented and used to calculate the UAV's attitude. Then we use appearance images to obtain a visual compass, and we calculate the relative rotation and heading of the aerial vehicle. Additionally, we show the use of a stereo system to calculate the aircraft height and to measure the UAV's motion. Finally, we present a visual tracking system based on Fuzzy controllers working in both a UAV and a camera pan and tilt platform. Every part is tested using the UAV COL-IBRI platform to validate the different approaches, which include comparison of the estimated data with the inertial values measured onboard the helicopter platform and the validation of the tracking schemes on real flights.

Vision Based Fuzzy Control Approaches for Unmanned Aerial Vehicles

“…A non-symmetric membership function is defined by non-equal sizes for the fuzzy sets of this variable. It has been proved in [18], [19], [20] that a special definition of the quantity and size of the fuzzy sets in the different variables can improve the behavior of a Fuzzy controller.…”

Autonomous Guided Car Using a Fuzzy Controller

“…With this software, it is possible to easily define a fuzzy controller with the required number of inputs and to select the type of membership functions, the defuzzification model and the inference operator. A more detailed explanation of this software can be found in [15]. The controller has two inputs and one output.…”

“…Anytime the weight of a rule becomes negative the system sets the output of the rule to the one given by the human driver. Further details of the software are given at [15]. Table 1 shows the base of rules which enclose the knowledge of the controller.…”

Tracking control of a UAV with a parallel visual processor

“…Prior work demonstrating visual-based tracking has shown the potential of solely visual based tracking [16] (this algorithm operates on a restricted number of pixels from a frame at 10fps) and pose estimation [17] (designed to operate at 15fps). Related visual based UAV cueing has shown that visual odometry has been achieved at 25fps on a two-camera setup with a 1.5GHz Core 2 Duo processor [18].…”