Christopher Eriksen

About

Christopher Eriksen is a leading researcher at the intersection of robotics, computer vision, and autonomous flight, with a core focus on enabling small unmanned aerial vehicles (UAVs) to navigate complex, cluttered environments using minimal, low-cost sensors. His most impactful contribution is pioneering the first implementation of receding horizon control for monocular vision-based flight, a breakthrough that allows agile quadcopters to plan and execute deliberate trajectories through dense obstacles using only a single camera. This work, detailed in his highly cited 2016 paper (46 citations), demonstrated that passive, lightweight sensors could replace expensive LiDAR systems for agile flight. Eriksen is also known for democratizing aerial robotics, showing how accessible platforms like the AR.Drone2 can perform sophisticated 3D navigation through clever image-matching techniques. More recently, he has tackled the critical challenge of data efficiency in robotics, developing frameworks that allow physical robots to learn object classifiers with minimal human supervision—directly addressing the labeling bottleneck that hinders deep learning in real-world applications. His work continues to push the boundaries of what small, intelligent drones can achieve.

Research Focus

Key Achievements

Top Papers

1. 1
   Vision and Learning for Deliberative Monocular Cluttered Flight

   46 citations · 2016
2. 2
   Vision and Learning for Deliberative Monocular Cluttered Flight

   9 citations · 2014
3. 3
   Accessible aerial robotics

   8 citations · 2014
4. 4
   Learning Object Classifiers with Limited Human Supervision on a Physical Robot

   4 citations · 2018

Key Collaborators

Contact & Links