Using a new field of applied mathematics, a computer scientist at The University of Texas at Arlington is working to enhance the perception capabilities of robots.
William Beksi, assistant professor of computer science and engineering, is investigating how to effectively process 3D point cloud data captured from low-cost sensors--information that robots could use to facilitate intelligent tasks in complex scenarios. Beksi's work is funded with a two-year, $175,000 grant from the National Science Foundation.
Three-dimensional point clouds are sets of points in space, sometimes with color information, that can be obtained from inexpensive 3D sensors. However, data generated by these sensors can suffer from anomalies, such as the presence of noise and variation in density of the points. These issues limit the reliability, efficiency and scalability of robotic perception applications that use 3D point clouds for manipulation, navigation, and object detection and classification.
"As 3D-sensor technology becomes pervasive in robotics, modern approaches to process and make use of this data in innovative and meaningful ways has not kept up," Beksi said. "Traditional 2D image-processing routines for extracting perceptually meaningful information cannot be directly applied to computer science or computer engineering.
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