Research Projects
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| Title | Abstract |
Start
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End |
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CONSTRUCT: Construction Site Monitoring and Change Detection using UAVs
(details) |
The goal of the project is to develop methods for modeling and surveying large construction sites. The project will make use of unmanned aerial vehicles and existing stationary or pan-tilt zoom cameras at the construction site. The goal is to provide accurate 3D models on a regular basis of the whole site. This will generate a 4D data set (3D+time). This data can then be used for documentation, visualization (we will use a mobile augmented reality system to overlay e.g. the plan or a model of the building) as well as measurement (e.g., how much material has been transported). From a scientific point of view we will have to solve following tasks:
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2011 | 2014 |
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PEGASUS: Autonomous Inspection of Overhead Power Lines using an Unmanned Aerial Vehicle
(details) |
The aim of the PEGASUS project is to develop a mobile vision system for overhead power line inspection to be mounted on an unmanned aerial vehicle (UAV). The long term goal is to develop a fully autonomous aerial vehicle which is able to perform power line inspection in an automated manner. This goal requires innovative solutions to a number of problems such as visual navigation, visual tracking and obstacle detection, model-based inspection under harsh conditions etc. In addition, due to the use of a small scale UAV (e.g. a quad-rotor helicopter) we have restricted computational resources for algorithms that need to be executed on the UAV (especially for navigation and tracking). Within PEGASUS we want to make significant progress towards this long term goal. In particular, PEGASUS will provide a set of tools for the inspector. The project is organized in four phases: First, an inspection system for a single power tower is developed. Used in ground-based inspection, the UAV provides close-up views of all points of interest from an optimal viewpoint. Second, we want to implement an automatic visual inspection system which highlights possible faulty components. In a third step, the system is extended towards multiple towers (still in the sight of the operator). Finally, the system will be used as a handheld system in manned helicopters by power line inspectors, where it should dramatically reduce the time needed for inspection. From a research perspective we will develop novel solutions for model-based recognition and pose estimation, visual navigation including obstacle avoidance and automated model-based visual inspection. All of these problems are extremely challenging because of the uncontrolled conditions (illumination etc.) and the real-time requirements. If successful, the methods developed in PEGASUS will be usable beyond the task of power line inspection. |
2010 | 2013 |
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Vision Based Kinematic Calibration and Error Compensation of Articulated Robot Arms
(details) |
Development of a vision system for accurate calibration of the kinematic chain of an articulated robot arm. The absolute positioning error of articulated robot arms is typically by an order of ten higher than their repeatability error. Inaccurate blueprint kinematic models typically account for 90% of this discrepancy. In this work a calibration procedure is developed which calibrates the kinematic model of a robot arm using fixed stereo rig and a calibration target mounted on the robot hand. In a single calibration framework the following parameters are automatically determined:
The procedure is fully automatic and does not require expensive, precalibrated equipment. |
2005 | 2006 |
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Robotics and Computer Vision Laboratory
(details) |
The Robot Vision Laboratory was established in 2004 to provide a common platform for experiments, demos and prototyping in the field of computer vision. Among other things, the lab inventory contains a 6DOF articulated robot arm, a PeopleBot mobile robot platform, several imaging and illumination devices. |
2004 | 2010 |
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CONEX
(details) |
Robust and Adaptive Approaches to Scene and Object Recognition: The goal of this joint project is to investigate new robust and adaptive approaches in the area of object and scene recognition. Object and scene recognition is a necessary requirement for developing truly cognitive systems as well as for the development of advanced and novel multimodal interfaces leading to ambient intelligence. Having a robust object and scene recognition system the following applications will greatly benefit: novel user interfaces which understand human activities, intelligent surveillance, indexing multi-media databases and content analysis of images, autonomous mobile systems and robotics, industrial inspection and robotics, etc. The goal is to develop computer vision based systems that can recognize objects, and in the context of environment perform localization and navigation. The major challenge is to develop systems and methods that can work under realistic unconstrained conditions (i.e., outside the lab). The three partners proposing this project (Center for Machine Perception, Czech Technical University Prague, CMP, Computer Vision Lab, Faculty of Computer and Information Science, University of Ljubljana, CVL, and Institute for Computer Graphics and Vision, Graz University of Technology ICG) have considerable expertise in this area and developed complementary methods and techniques. The goal of the project is to join the efforts and combine the expertise. In particular, we do the following activities:
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2003 | 2005 |
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Visual Pose Determination by a Robot in six degrees of freedom
(details) |
We propose to develop a robotic system for the exact pose estimation of rigid objects in six degrees of freedom (DoF). Our approach is based on the following scenario: Given be a set of unordered objects in arbitrary position which come from a small variety (<10) of different types as may happen on a conveyor belt during an assembly process. We assume the objects to be mostly un-occluded and their CAD-model given. Our goal is to pick with a robot a specific object in such a way that it has a defined position. The robot is equipped with multiple black and white cameras (e.g., 2-3). In order to achieve maximum generality we do not intend to use range information nor calculate a 3D-representation from stereo images. This task requires a solution for the following sub-problems: Recognition and initial pose estimation, which will be approached using robust appearance-based recognition methods; Pose refinement will be handled by model-based methods; Movement planning to enhance the accuracy of the pose estimation. Partnerlist: |
2001 | 2002 |
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Mobile Robotics
(details) |
Research topics on mobile robotics: -Visual localisation and map building -Usage of local visual landmarks -Localisation using omnidirectional camera |
2001 | 2005 |
