Real-time video stabilization without phantom movements for micro aerial vehicles

In recent times, micro aerial vehicles (MAVs) are becoming popular for several applications as rescue, surveillance, mapping, etc. Undesired motion between consecutive frames is a problem in a video recorded by MAVs. There are different approaches, applied in video post-processing, to solve this issue. However, there are only few algorithms able to be applied in real time. An additional and critical problem is the presence of false movements in the stabilized video. In this paper, we present a new approach of video stabilization which can be used in real time without generating false movements. Our proposal uses a combination of a low-pass filter and control action information to estimate the motion intention.Peer ReviewedPostprint (published version

Real-time model-based video stabilization for microaerial vehicles

The emerging branch of micro aerial vehicles (MAVs) has attracted a great interest for their indoor navigation capabilities, but they require a high quality video for tele-operated or autonomous tasks. A common problem of on-board video quality is the effect of undesired movements, so different approaches solve it with both mechanical stabilizers or video stabilizer software. Very few video stabilizer algorithms in the literature can be applied in real-time but they do not discriminate at all between intentional movements of the tele-operator and undesired ones. In this paper, a novel technique is introduced for real-time video stabilization with low computational cost, without generating false movements or decreasing the performance of the stabilized video sequence. Our proposal uses a combination of geometric transformations and outliers rejection to obtain a robust inter-frame motion estimation, and a Kalman filter based on an ANN learned model of the MAV that includes the control action for motion intention estimation.Peer ReviewedPostprint (author's final draft

Motion intention optimization for multirotor robust video stabilization

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksIn this paper we present an optimization algorithm for simultaneously detecting video freeze and obtaining the minimum number of the frame required in motion intention estimation for real time robust video stabilization on multirotor unmanned aerial vehicles. A combination of a filter and a threshold is used to the video freeze detection, and for optimizing the algorithm, we find the minimum number of frames for motion intention estimation without decrease the performance.Peer ReviewedPostprint (author's final draft

Obstacle avoidance based-visual navigation for micro aerial vehicles

This paper describes an obstacle avoidance system for low-cost Unmanned Aerial Vehicles (UAVs) using vision as the principal source of information through the monocular onboard camera. For detecting obstacles, the proposed system compares the image obtained in real time from the UAV with a database of obstacles that must be avoided. In our proposal, we include the feature point detector Speeded Up Robust Features (SURF) for fast obstacle detection and a control law to avoid them. Furthermore, our research includes a path recovery algorithm. Our method is attractive for compact MAVs in which other sensors will not be implemented. The system was tested in real time on a Micro Aerial Vehicle (MAV), to detect and avoid obstacles in an unknown controlled environment; we compared our approach with related works.Peer ReviewedPostprint (published version

3D environment mapping using the Kinect V2 and path planning based on RRT algorithms

This paper describes a 3D path planning system that is able to provide a solution trajectory for the automatic control of a robot. The proposed system uses a point cloud obtained from the robot workspace, with a Kinect V2 sensor to identify the interest regions and the obstacles of the environment. Our proposal includes a collision-free path planner based on the Rapidly-exploring Random Trees variant (RRT*), for a safe and optimal navigation of robots in 3D spaces. Results on RGB-D segmentation and recognition, point cloud processing, and comparisons between different RRT* algorithms, are presented.Peer ReviewedPostprint (published version

The use of modern tools for modelling and simulation of UAV with Haptic

Unmanned Aerial Vehicle (UAV) is a research field in robotics which is in high demand in recent years, although there still exist many unanswered questions. In contrast, to the human operated aerial vehicles, it is still far less used to the fact that people are dubious about flying in or flying an unmanned vehicle. It is all about giving the control right to the computer (which is the Artificial Intelligence) for making decisions based on the situation like human do but this has not been easy to make people understand that it’s safe and to continue the enhancement on it. These days there are many types of UAVs available in the market for consumer use, for applications like photography to play games, to map routes, to monitor buildings, for security purposes and much more. Plus, these UAVs are also being widely used by the military for surveillance and for security reasons. One of the most commonly used consumer product is a quadcopter or quadrotor. The research carried out used modern tools (i.e., SolidWorks, Java Net Beans and MATLAB/Simulink) to model controls system for Quadcopter UAV with haptic control system to control the quadcopter in a virtual simulation environment and in real time environment. A mathematical model for the controlling the quadcopter in simulations and real time environments were introduced. Where, the design methodology for the quadcopter was defined. This methodology was then enhanced to develop a virtual simulation and real time environments for simulations and experiments. Furthermore, the haptic control was then implemented with designed control system to control the quadcopter in virtual simulation and real time experiments. By using the mathematical model of quadcopter, PID & PD control techniques were used to model the control setup for the quadcopter altitude and motion controls as work progressed. Firstly, the dynamic model is developed using a simple set of equations which evolves further by using complex control & mathematical model with precise function of actuators and aerodynamic coefficients Figure5-7. The presented results are satisfying and shows that flight experiments and simulations of the quadcopter control using haptics is a novel area of research which helps perform operations more successfully and give more control to the operator when operating in difficult environments. By using haptic accidents can be minimised and the functional performance of the operator and the UAV will be significantly enhanced. This concept and area of research of haptic control can be further developed accordingly to the needs of specific applications

The use of modern tools for modelling and simulation of UAV with Haptic

Unmanned Aerial Vehicle (UAV) is a research field in robotics which is in high demand in recent years, although there still exist many unanswered questions. In contrast, to the human operated aerial vehicles, it is still far less used to the fact that people are dubious about flying in or flying an unmanned vehicle. It is all about giving the control right to the computer (which is the Artificial Intelligence) for making decisions based on the situation like human do but this has not been easy to make people understand that it’s safe and to continue the enhancement on it. These days there are many types of UAVs available in the market for consumer use, for applications like photography to play games, to map routes, to monitor buildings, for security purposes and much more. Plus, these UAVs are also being widely used by the military for surveillance and for security reasons. One of the most commonly used consumer product is a quadcopter or quadrotor. The research carried out used modern tools (i.e., SolidWorks, Java Net Beans and MATLAB/Simulink) to model controls system for Quadcopter UAV with haptic control system to control the quadcopter in a virtual simulation environment and in real time environment. A mathematical model for the controlling the quadcopter in simulations and real time environments were introduced. Where, the design methodology for the quadcopter was defined. This methodology was then enhanced to develop a virtual simulation and real time environments for simulations and experiments. Furthermore, the haptic control was then implemented with designed control system to control the quadcopter in virtual simulation and real time experiments. By using the mathematical model of quadcopter, PID & PD control techniques were used to model the control setup for the quadcopter altitude and motion controls as work progressed. Firstly, the dynamic model is developed using a simple set of equations which evolves further by using complex control & mathematical model with precise function of actuators and aerodynamic coefficients Figure5-7. The presented results are satisfying and shows that flight experiments and simulations of the quadcopter control using haptics is a novel area of research which helps perform operations more successfully and give more control to the operator when operating in difficult environments. By using haptic accidents can be minimised and the functional performance of the operator and the UAV will be significantly enhanced. This concept and area of research of haptic control can be further developed accordingly to the needs of specific applications