Sensor Modeling and Multi-Sensor Data Fusion
Abstract
This research report presents a novel strategy to develop a sensor model based on a probabilistic approach that would accurately provide information about individual sensor's uncertainties and limitations. The strategy also establishes the dependence of sensor's uncertainties on some of environmental parameters or parameters of any feature extraction algorithm used in estimation based on sensor's outputs. The approach makes use of a neural network that is trained with the help of an innovative technique that obtains training signal from a maximum likelihood estimator. The proposed technique was applied for modeling stereo-vision sensors and an Infra-Red (IR) proximity sensor used in the robotic work cell available in the Robotics and Manufacturing Automation (RAMA) Laboratory at Duke University. In addition, the report presents an innovative method to fuse the probabilistic information obtained from these sensors based on Bayesian formalism in an occupancy grid framework to obtain three-dimensional occupancy model and key features of the robotic workspace. The capability of the proposed technique in accurately obtaining three-dimensional occupancy profile and efficiently removing individual sensor uncertainties was validated and compared with other methods via experiments carried out in the RAMA lab during this project.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 26, 2005
- Accession Number
- ADA440553
Entities
People
- Devendra P. Garg
- Manish Kumar
Organizations
- Duke University