*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
Atlanta, GA | Posted: June 9, 2020
Allie McFadden
Communications Officer
allie.mcfadden@cc.gatech.edu
Summary Sentence:
The award recognizes the most influential ICRA paper published between 1998-2002 and selected Monte Carlo Localization for Mobile Robots as this year’s recipient.
Full Summary:
No summary paragraph submitted.
Frank Dellaert, a professor in the School of Interactive Computing, and affiliated with the Machine Learning Center at Georgia Tech (ML@GT) and GVU Center, has been honored with the IEEE ICRA Milestone Award at the 2020 IEEE International Conference on RoFrank Dellaert, a professor in the School of Interactive Computing, and affiliated with the Machine Learning Center at Georgia Tech (ML@GT) and GVU Center, has been honored with the IEEE ICRA Milestone Award at the 2020 IEEE International Conference on Robotics and Automation (ICRA.)
The award recognizes the most influential ICRA paper published between 1998-2002 and selected Monte Carlo Localization for Mobile Robots as this year’s recipient. Dellaert conducted this work during his Ph.D studies at Carnegie Mellon University with Dieter Fox, Wolfram Burgard, and Sebastian Thrun.
“It is a great honor to be recognized, but receiving a ’20 years on’ milestone award also makes you feel old!” said Dellaert.
The paper was accepted to ICRA in 1999 and introduced the Monte Carlo Localization (MLC) method or particle filter localization, which represents the probability density involved in maintaining a set of samples that are randomly drawn from it. This method is faster, more accurate, and less memory-intensive than earlier grid-based methods and allows a robot to be localized without knowledge of its starting location.
MCL is simple to apply to the robotics domain, leading to its popularity. It is now taught in every robotics 101 class around the world. Many mobile robots, including commercial efforts, rely on MCL for localizing.
“Simplicity is key for acceptance and you cannot predict which of your research will have the most impact. This paper was a result of me procrastinating on my Ph.D. thesis which is a paper almost nobody read. It is an enormous honor that MCL has made a lasting impact on our field,” said Dellaert.
