Jacob is currently working on complete-coverage path planning over rough 3-D terrain for wilderness search and rescue. The algorithm he has developed uses directional offset curves from a reference contour to generate flyable paths in 3 dimensions that assure both the safety of the sensor platform, and complete coverage of the search region with the sensor.

Brandon worked on detecting GPS spoofing attacks based on the video coming from a camera on a UAV. GPS spoofing attacks cause the GPS receiver to give false readings to make the UAV think that it is somewhere it is not. Spoofing can potentially be detected by calculating the optical flow in the video and making sure that it is consistent with the movement reported by the GPS. Also, significant differences in the terrain between two locations may cause detectable differences between the actual optical flow and the expected motion field.

Previously, Brandon worked on developing a video quality metric for use in surveillance scenarios. The metric can be used to predict the quality of video that will be obtained from a given flight path as well as evaluate actual video quality attained after flight.

John’s research interests are in the area of sensor-based navigation alternatives to GPS.  While at BYU he was in the MAGICC Lab’s Indoor Navigation group.  John received his BSEE from BYU in 2002 and his MSEE from the Air Force Institute of Technology (AFIT) in 2006.

Bryce worked on the simultaneous localization and detection and tracking of moving objects using a laser range finder on an aerial robotic platform. He was funded by the Department of Defense's SMART Scholarship.

Bryce's research included work on super-resolution and mosaicking, image registration, and visual odometry.  One way to visualize and utilize UAV video is to convert the noisy, jittery video stream from the UAV into a 2-D map of the terrain over which the craft is flying.  Pasting multiple video frames together to make this map is called mosaicking.  Because the same spot of ground is usually seen by several video frames, "super-resolution" techniques can be used to make a mosaic that has higher resolution (and hence is clearer and shows more detail) than any of the individual video frames.  Bryce has developed a mosaicking tool that can generate a super-resolved mosaic from a video stream.  Evan Anderson got this algorithm working with real UAV video.

In order to mosaick video frames together, we first need to know how those frames line up with each other, so that we can stitch them together seamlessly.  The process of finding out how two images line up with each other is called image registration.  A fortunate by-product of registration is that it allows the video stream to be used to very accurately determine the position and the attitude (pitch, roll, heading) of the UAV.  Bryce is worked on exploiting this fact to improve UAV position and attitude estimates, a task known as visual odometry.  This will allow the super-resolved mosaic built from a video to be accurately geo-referenced

Liang Sun received his B.S. and M.S. degrees from Beihang University, China, in 2004 and 2007, respectively. In 2012 he completed his Ph.D. in the Department of Electrical and Computer Engineering at Brigham Young University.

Research areas: Cooperative control of unmanned air vehicles, nonlinear control, dynamic modeling, trajectory tracking, path following, backstepping