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

FRC Seminar

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

Kevin Peterson
PhD Student, Robotics Institute, Carnegie Mellon University


Thursday, May 7th, 2009

Developers of autonomous vehicles must overcome significant challenges before these vehicles can operate around human drivers. In urban environments autonomous cars will be required to follow complex traffic rules regarding merging and queuing, navigate in close proximity to other drivers, and safely avoid collision with pedestrians and fixed obstacles near the road. Knowledge of the location and shape of the roadway near then autonomous car is fundamental to these behaviors. While it is tempting to build an /a priori /GPS-registered map of the road network, the possibility for change in the road structure (e.g. new roads, construction, etc.) precludes the use of maps alone. It is therefore necessary to detect and track roads in real-time.

A rich body of work exists in the area of road tracking. Although some early work was performed on unimproved roads, a majority of the available research focuses on paved roads and highways. Additionally, a vast majority of this work focuses on the use of cameras as the single sensing modality. In this talk I will present a framework for road tracking that uses a particle filter to fuse several sources of data including LIDAR and video. The approach enables road tracking on unimproved roads and, because of the flexible nature of the particle filter, can easily be extended to incorporate new forms of data. I will present results from our preparations for the DARPA Urban Challenge.

Speaker Bio: Kevin Peterson’s research focuses on perception techniques for robust autonomous vehicles. Kevin holds a B.S. and M.S. in Electrical and Computer Engineering from Carnegie Mellon University in Pittsburgh, Pennsylvania and is currently pursuing a PhD in Robotics also from Carnegie Mellon University. Kevin has built software systems for many autonomous systems with applications ranging from cave exploration to unexploded ordinance cleanup. Notably, Kevin led Red Team Too, one of CMUs entries in the 2005 DARPA Grand Challenge and was a participant on Tartan Racing, CMUs winning entry in the 2007 Urban Grand Challenge. Since then, he has been applying inverse optimal control techniques to build models of pedestrian motion in structured and unstructured environments.