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Daniel Huber
Senior Systems Scientist, RI
Email:
Office: EDSH 217
Phone: (412) 268-2991
Fax: 412-268-6436
  Mailing address:
Carnegie Mellon University
Robotics Institute
5000 Forbes Avenue
Pittsburgh, PA 15213
Administrative Assistant: Jessica Butterbaugh
Affiliated Center(s):
 Vision and Autonomous Systems Center (VASC)
Personal Homepage

Past Projects [Current Projects]
 
3D Terrain Mapping
We are developing methods for building large-scale, topographic maps of unstructured outdoor environments.
Advanced Sensor Based Defect Management at Construction Sites (ASDMCon)
This research project builds on, combines and extends the advances in generating 3D environments using laser scanners.
Automatic 3D Modeling from Range Images
A system for generating 3D models of real-world objects without manual or mechanical aids.
CTA Robotics
This project adresses the problems of scene interpretation and path planning for mobile robot navigation in natural environment.
Exploitation of 3-D Data (E3D)
The E3D project will develop technology to detect, characterize and recognize vehicular targets in 3-D data.
LIDAR and Vision Sensor Fusion for Autonomous Vehicle Navigation
The goal of this project is to investigate methods for combining laser range sensors (i.e., LIDARs) with visual sensors (i.e., video cameras) to improve the capabilities of autonomous vehicles.
Optimal LIDAR Sensor Configuration
This project is developing a framework that allows objective comparison between alternative LIDAR configurations.
Real-time Lane Tracking in Urban Environments
The purpose of this project is to develop methods for the real-time detection and tracking of lanes and intersections in urban scenarios in order to support road following by an autonomous vehicle in GPS-denied situations.
Terrain Classification
Using a multispectral camera, we classify terrain into categories such as grass, dirt, trees, and rock.
Terrain Estimation using Space Carving Kernels
This project uses information about the ray extending from the sensor to the sensed surface be used to improve terrain estimation in unstructured environments.
Tightly Integrated Stereo and LIDAR
The goal of this project is to use sparse, but accurate 3D data from LIDAR to improve the estimation of dense stereo algorithms in terms of accuracy and speed.
Unmanned Ground Vehicles
Developing autonomous navigation capabilities for mobile robots driving in complex, unstructured outdoor terrain.