Andrew Bainbridge-Smith

About

Andrew Bainbridge-Smith is a robotics researcher whose work centers on autonomous flight control, visual-inertial odometry, and biologically inspired navigation for unmanned aerial vehicles (UAVs). His most influential contribution is pioneering the use of the low-cost Microsoft Kinect sensor for quadrotor helicopter altitude control, demonstrating how consumer-grade depth maps could enable reliable autonomous hovering—a paper that has garnered 126 citations and helped democratize drone research. He further advanced UAV autonomy by developing monocular optical flow and divergence templates for obstacle avoidance, mimicking insect vision to enable collision-free flight without expensive sensors. Bainbridge-Smith also contributed to robust relative pose estimation by fusing inertial measurement unit (IMU) data with SURF feature matching, improving efficiency in visual navigation systems. More recently, he has addressed the critical challenge of creating realistic outdoor visual-inertial odometry datasets through an improved semi-synthetic approach, providing essential tools for developing resilient VIO algorithms. His work bridges practical engineering with biological principles, making autonomous flight more accessible and reliable for the broader robotics community.

Research Focus

Key Achievements

Top Papers

1. 1
   Altitude control of a quadrotor helicopter using depth map from Microsoft Kinect sensor

   126 citations · 2011
2. 2
   Biologically inspired UAV obstacle avoidance and control using monocular optical flow & divergence templates

   7 citations · 2011
3. 3
   Quadrotor Helicopter Flight Control Using Hough Transform and Depth Map from a Microsoft Kinect Sensor

   6 citations · 2011
4. 4
   IMU-aided SURF feature matching for relative pose estimation

   3 citations · 2010
5. 5
   An improved semi-synthetic approach for creating visual-inertial odometry datasets

   2 citations · 2022

Key Collaborators

Contact & Links