References of "Chen, Yangquan"
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See detailVisual Attitude Estimation for Low-Cost Personal Remote Sensing Systems
Fromm, Tobias; Di, Long; Chen, Yangquan et al

in 7th International ASME/IEEE Conference on Mechatronics & Embedded Systems & Applications MESA 2011, Washington 28-31 August 2011 (2011, August 29)

Remote Sensing using unmanned aerial vehicles (UAV) is gathering a lot of attention at the moment by researchers and developers, especially in terms of low-cost aircrafts which still maintain sufficient ... [more ▼]

Remote Sensing using unmanned aerial vehicles (UAV) is gathering a lot of attention at the moment by researchers and developers, especially in terms of low-cost aircrafts which still maintain sufficient accuracy and performance. This paper introduces a low-cost approach to increase airworthiness by using a forward-looking camera to estimate the attitude of a UAV. It not only focuses on using machine learning to classify ground and sky, but also uses image processing and software engineering methods to make it fault-tolerant and really applicable on a miniature UAV. Additionally, it is able to interface with an autopilot framework to being used productively on flight missions. [less ▲]

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See detailIn-Situ Unmanned Aerial Vehicle Sensor Calibration to Improve Automatic Image Orthorectification
Jensen, Austin; Wildmann, Norman; Chen, Yangquan et al

in IEEE Int. Geoscience and Remote Sensing Symposium; honolulu, USA, 2010 (2010)

Small, low-altitude unmanned aerial vehicles (UAV)s can be very useful in many ecological applications as a personal remote sensing platform. However, in many cases it is difficult to produce a single ... [more ▼]

Small, low-altitude unmanned aerial vehicles (UAV)s can be very useful in many ecological applications as a personal remote sensing platform. However, in many cases it is difficult to produce a single georeferenced mosaic from the many small images taken from the UAV. This is due to the lack of features in the images and the inherent errors from the inexpensive navigation sensors. This paper focuses on improving the orthorectification accuracy by finding these errors and calibrating the navigation sensors. This is done by inverseorthorectifying a set of images collected during flight using ground targets and General Procrustes Analysis. By comparing the calculated data from the inverse-orthorectification and the measured data from the navigation sensors, different sources of errors can be found and characterized, such as GPS computational delay, logging delay, and biases. With this method, the orthorectification errors are reduced from less than 60m to less than 1.5m. [less ▲]

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