Evan Herbst
Papers
6
Total Citations
2,538
H-Index
6
About
Evan Herbst is a leading figure in robotics and computer vision, best known for pioneering the use of RGB-D cameras—like the Microsoft Kinect—for dense 3D mapping of indoor environments. His seminal work, *RGB-D Mapping: Using Kinect-style Depth Cameras for Dense 3D Modeling of Indoor Environments*, has amassed over 1,170 citations and remains a foundational reference for robot navigation and manipulation. Herbst’s contributions extend beyond mapping; he developed novel methods for dense 3-D motion estimation (RGB-D flow, 149 citations) and advanced human-robot interaction by enabling robots to parse natural language commands into control systems (327 citations). His research also explores object discovery and modeling through 3-D scene comparison (75 citations), enhancing robots’ ability to understand and adapt to their surroundings. With a focus on fusing color and depth data, Herbst’s work has significantly advanced autonomous systems’ perception and interaction capabilities. His achievements include real-time face and object tracking, demonstrating his commitment to practical, deployable solutions. For students and researchers, Herbst’s career exemplifies how innovative sensing and algorithmic design can transform robotic autonomy.
Research Focus
Key Achievements
Top Papers
- 1
- 2RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments811 citations · 2013
- 3Learning to Parse Natural Language Commands to a Robot Control System327 citations · 2013
- 4RGB-D flow: Dense 3-D motion estimation using color and depth149 citations · 2013
- 5Toward object discovery and modeling via 3-D scene comparison75 citations · 2011
- 6Real-time face and object tracking6 citations · 2009