Research Experience

Past

NASA SEES — Drone Position Estimation

Through the NASA and University of Texas Center for Space Research STEM Enhancement in Earth Science (SEES) program, I collaborated on an aerial robotics research project focused on improving drone position estimation. By combining Inertial Measurement Unit (IMU) and LiDAR data through sensor fusion and advanced noise-filtering algorithms, our team worked to drastically improve flight localization accuracy on experimental drones and unmanned aerial vehicles (UAVs).

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Project goals

  • Investigated precise drone localization by implementing sensor fusion algorithms combining onboard Inertial Measurement Unit (IMU) and LiDAR data.
  • Assembled the experimental quadcopter mechanical chassis, configured the open-source flight software stack, and served as the primary flight test pilot for empirical data collection.
  • Programmed a data processing pipeline in Python using pyulog to extract binary flight logs, applying a Kalman Filter that successfully reduced localization sensor noise by 24%.
  • Showcased project outcomes via a research presentation at the American Geophysical Union (AGU) 2024 Annual Meeting in Washington, D.C.

Project gallery

NASA SEES team in front of AGU sign
Configuring the Drone
NASA SEES group photo at AGU
Group Photo at AGU
NASA Johnson Space Center training facility
Johnson Space Center Tour
Working with a drone in the lab
Drone Assembly
NASA SEES team at AGU event backdrop
AGU Signage
Space Center Houston exhibit visit
Space Center Houston Visit
NASA career pathway presentation slide
Talk from NASA Flight Director Zebulon Scoville!
SEES team and drone systems in the lab
Full Team Photo
Hands-on drone assembly and testing
Experimental Picosatellite called Z-Qube

Research Team Presentation

NASA SEES Experience Review