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Position and 3D orientation estimation

Direct Methods for Pose Estimation of Aerial Vehicles

Direct Methods for Pose Estimation of Aerial Vehicles
A Multi-Resolution strategy of an image alignment method is used to solve the pose estimation problem at real-time frame rates. With this strategy, it is possible to provide the system with a good approximation of the vehicle's state during long periods of time (minutes), basing the estimation only on visual information.
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A Multi-Resolution strategy of an image alignment method is used to solve the pose estimation problem at real-time frame rates. With this strategy, it is possible to provide the system with a good approximation of the vehicle's state during long periods of time (minutes), basing the estimation only on visual information.

Robust Stereo Visual Odometry and SLAM for Unmanned Aerial Vehicles

Robust Stereo Visual Odometry and SLAM for Unmanned Aerial Vehicles
Robust Stereo Visual Odometry and SLAM for Unmanned Aerial Vehicles
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Stereo Visual Odometry and SLAM

 

A new light small-scale low-cost ARM-based Stereo Vision Pre-Processing System is designed.

            

           

Researches: 

Robust Dynamic RGB-D Localization and Mapping for UAVs

Robust Dynamic RGB-D Localization and Mapping for UAVs
This reseach aims to provide the fast and robust visual algorithm for UAV to fly in the GPS-denied environment with high speed. The whole system consists of Asctec Pelican or DJI F550 UAV platform (hexcopter), Pixhawk, IntelNUC (Odroid) and RGB-D Sensor (Asus Xtion Pro Live). The real-time 6D pose is estimated by visual SLAM-based algorithm. The test environment includes: (1) Corridors; (2) Square with Obstacles; (3) Lab (Long-term); (4) School Entrance (Long-term); (5) Parking Places et al. The comparison with VICON and real flight show that the visual SLAM algorithm is accurate and robust.
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Robust Dynamic RGB-D Localization and Mapping for UAVs

 

Asctec Pelican Quadrotor

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Researches: 

Monocular Visual-Inertial SLAM-based Collision Avoidance Strategy for Fail-Safe UAV using Fuzzy Logic Controllers

Journal: 
Journal of Intelligent & Robotic Systems
Publication Date: 
2014

In this paper, we developed a novel Cross-Entropy Optimization (CEO)-based Fuzzy
Logic Controller (FLC) for Fail-Safe UAV to expand its collision avoidance capabilities
in the GPS-denied envrionments using Monocular Visual-Inertial SLAM-based
strategy. The function of this FLC aims to control the heading of Fail-Safe UAV to avoid
the obstacle, e.g. wall, bridge, tree line et al, using its real-time and accurate
localization information. In the Matlab Simulink-based training framework, the Scaling

IMAV 2013 Competition

IMAV 2013 Competition
IMAV 2013 Competition
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 We are developing algorithms and preparing our quadrotors to participate in the IMAV 2013 indoors competition: http://www.imav2013.org/.

Computer Vision System for autonomous landing of VTOL RPAS

Computer Vision System for autonomous landing of VTOL RPAS
For the autonomous landing of VTOL RPAS, we use a computer vision system what is cheaper than a LIDAR and allow us to measure the pose of the helipad respect to the RPAS with accuracy enough.
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For the autonomous landing of VTOL RPAS, we use a computer vision system what is cheaper than a LIDAR and allow us to measure the pose of the helipad respect to the RPAS with accuracy enough.

We assume that the heliport is a common and most extended helipad with a white H surrounded by a white circle, painted on the gray heliport surface.

Ship deck simulation for autonomous landing of VTOL RPAS

Ship deck simulation for autonomous landing of VTOL RPAS
The autonomous landing of VTOL PRAS on Ships is a challenger problem. To test a controller, the first step is develop a ship deck simulator with the six degrees of freedom.
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To design and test a controller for autonomous landing of VTOL UAVs on ships, it is necessary to calculate how the ship moves in the Sea, using the whole six degrees of freedom:

Asynchronous Extended Kalman Filter

Asynchronous Extended Kalman Filter
This Asynchronous Extended Kalman Filter is an extension of the EKF that can easily combine measures received at different rates.
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The Extended Kalman Filter (EKF) allow us to estimate the state of a system, known the inputs of the system and the measurements of the outputs of the system. A non-linear model can be used with EKFs. However, if the outputs of the system don't have the same rate, a non-linear model for each measurements state is needed.

This problem can be easily solved by modifying the equations of the Output Prediction, Output Matching and State Correction Steps, by introducing a binary vector of the enabled outputs measures.

State estimation for the path-Following control problem of visual unmanned ground systems

State estimation for the path-Following of visual unmanned ground systems
An state estimator for the path-Following control problem of unmanned car-like visual systems was developed. This state estimator is composed by five modules each one with a different mission.
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The state estimator for visually-guided car like mobile robots has the objective of analyse the measurements of provided by the unmaned ground platform and calculate the better estimation of the state of the vehicle to send it to the controller who closes the loop. This is one of the main systems of the Path following control system for visually guided unmanned ground systems.

3D Reconstruction for Unmanned Aerial Vehicle

3D Reconstruction for Unmanned Aerial Vehicle
3D Reconstruction using SLAM (SFM) algorithms and Point Cloud Library for Unmanned Aerial Vehicle can establish the whole real-time unknown environment in Ground Station (GS), this research aims to construct the 3D environment for (Map-based) indoor applications and explore the SLAM (SFM) techniques to future UAV (6D) Monocular or Stereo Visual Odometry researches. (Pose Estimation)
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3D Reconstruction

3D Reconstruction: (Extracting from the real-time video)

The results:

    

Researches: 
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