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Vision System Prototype for UAV Positioning and Sparse Obstacle Detection
Blekinge Institute of Technology, School of Engineering.
Blekinge Institute of Technology, School of Engineering.
2012 (English)Independent thesis Advanced level (degree of Master (Two Years))Student thesis
Abstract [en]

For the last few years computer vision due to its low exploitation cost and great capabilities has been experiencing rapid growth. One of the research fields that benefits from it the most is the aircrafts positioning and collision avoidance. Light cameras with low energy consumption are an ideal solution for UAVs (Unmanned Aerial Vehicles) navigation systems. With the new Swedish law – unique to Europe, that allows for civil usage of UAVs that fly on altitudes up to 120 meters, the need for reliable and cheap positioning systems became even more dire. In this thesis two possible solutions for positioning problem and one for collision avoidance were proposed and analyzed. Possibility of tracking the vehicles position both from ground and from air was exploited. Camera setup for successful positioning and collision avoidance systems was defined and preliminary results for of the systems performance were presented.

Abstract [sv]

Vision systems are employed more and more often in navigation of ground and air robots. Their greatest advantages are: low cost compared to other sensors, ability to capture large portion of the environment very quickly on one image frame, and their light weight, which is a great advantage for air drone navigation systems. In the thesis the problem of UAV (Unmanned Aerial Vehicle) is considered. Two different issues are tackled. First is determining the vehicles position using one down-facing or two front-facing cameras, and the other is sparse obstacle detection. Additionally, in the thesis, the camera calibration process and camera set up for navigation is discussed. Error causes and types are analyzed.

Place, publisher, year, edition, pages
2012. , 64 p.
Keyword [en]
UAV, navigation, positioning, vision system, collision avoidance
National Category
Computer Science Signal Processing
URN: urn:nbn:se:bth-4663Local ID: diva2:832010
Available from: 2015-04-22 Created: 2013-01-09 Last updated: 2015-06-30Bibliographically approved

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