Matthew N. Dailey
Papers
8
Total Citations
224
H-Index
4
About
Matthew N. Dailey is a leading researcher in computer vision and autonomous robotics, with a career spanning foundational work in visual simultaneous localization and mapping (SLAM) to cutting-edge imitation learning. His most impactful contribution is the widely-cited paper “Automatic Radial Distortion Estimation from a Single Image” (163 citations), which solved a critical problem in image correction for robotics and photography. Dailey’s early work on stereo vision SLAM (2006) pioneered methods for robots to build maps and localize using only video, a cornerstone of modern autonomous navigation. He has since advanced 3D environment reconstruction through occupancy grid isosurfaces and developed innovative tracking algorithms for quadcopter pursuit and target redetection using monocular cues. His recent work introduces Reinforced Intervention-based Imitation Learning (ReIL), a framework that dramatically improves sample efficiency in teaching robots complex behaviors by combining human intervention with reinforcement learning. Dailey’s research consistently bridges theoretical rigor with practical deployment, making him a key figure in enabling robots to perceive, navigate, and learn in real-world environments.
Research Focus
Key Achievements
Top Papers
- 1Automatic Radial Distortion Estimation from a Single Image163 citations · 2012
- 2Simultaneous Localization and Mapping with Stereo Vision28 citations · 2006
- 3Rapid 3D visualization of indoor scenes using 3D occupancy grid isosurfaces11 citations · 2009
- 4Joint Localization of Pursuit Quadcopters and Target Using Monocular Cues10 citations · 2014
- 5Model driven state estimation for target pursuit4 citations · 2012
- 6
- 7Joint localization and target tracking with a monocular camera3 citations · 2015
- 8ReIL: A Framework for Reinforced Intervention-based Imitation Learning2 citations · 2022