Fast Feature Detection and Stochastic Parameter Estimation of Road Shape using Multiple LIDAR

Kevin Peterson, Jason Ziglar, and Paul Rybski
IEEE/RSJ 2008 International Conference on Intelligent Robots and Systems, October, 2008.


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Abstract
This paper describes an algorithm for an autonomous car to identify the shape of a roadway by detecting geometric features via LIDAR. The data from multiple LIDAR are fused together to detect both obstacles as well as geometric features such as curbs, berms, and shoulders. These features identify the boundaries of the roadway and are used by a stochastic state estimator to identify the most likely road shape. This algorithm has been used successfully to allow an autonomous car to drive on paved roadways as well as on off- road trails without requiring different sets of parameters for the different domains.

Notes
Associated Center(s) / Consortia: Field Robotics Center
Associated Project(s): Urban Challenge and Tartan Racing
Number of pages: 8

Text Reference
Kevin Peterson, Jason Ziglar, and Paul Rybski, "Fast Feature Detection and Stochastic Parameter Estimation of Road Shape using Multiple LIDAR," IEEE/RSJ 2008 International Conference on Intelligent Robots and Systems, October, 2008.

BibTeX Reference
@inproceedings{Peterson_2008_6183,
   author = "Kevin Peterson and Jason Ziglar and Paul Rybski",
   title = "Fast Feature Detection and Stochastic Parameter Estimation of Road Shape using Multiple LIDAR",
   booktitle = "IEEE/RSJ 2008 International Conference on Intelligent Robots and Systems",
   publisher = "IEEE",
   month = "October",
   year = "2008",
}