Ivan Kirigin is a Software Engineer at iRobot Corporation specializing in computer vision and perception for mobile robots. Current projects include object tracking, image stabilization, super resolution, and LIDAR based obstacle avoidance, moving object detection, and simultaneous localization and mapping. Prior to joining iRobot Corp., Mr. Kirigin worked as a scientist for Charles River Analytics on automated nighttime video surveillance. Mr. Kirigin received his M.S. (2005) from the Robotics Institute at Carnegie Mellon University where he worked on visual odometry for extreme mobility platforms and received his B.A. (2003) from New York University in computer science with honors. His interests include aided teleoperation, stabilization, detection, identification, recognition, tracking, and simultaneous localization and mapping.
Computer Science-Carnegie Mellon University, focuses on the development of sensor processing and understanding for urban maneuverability and operational aspects such as localization, mapping, obstacle detection, target tracking, Kino-Dynamic Motion Planning and change detection. Most recently, Dr. Lenser was the lead investigator on the Sentinel project, a more than $725k SBIR Phase II effort. His innovations included the architectural design and implementation of path planning over a route network; obstacle detection and avoidance mechanisms; safe navigation in areas where GPS data is unavailable; and, behaviors to support safe following of other vehicles and identification of pedestrians. Prior to iRobot, Dr. Lenser led Carnegie Mellon’s champion team in the 2002 RoboCup international robot soccer competition.
Electrical Engineering, Vanderbilt University, is the lead robotics researcher for iRobot’s Government and Industrial division. Dr. Pack’s lengthy experience in designing autonomous and semi-autonomous robot systems includes navigation, vision, obstacle negotiation, Kino-Dynamic Motion Planning and mobile manipulation. He is a key developer on Wayfarer, an urban navigation system based on the PackBot™, which uses LIDAR and vision systems to perform mapping while autonomously exploring an urban environment. He was the Technical Lead for development of the PackBot™ EOD system, and a lead designer of iRobot Aware™ 2.0 robot software integration environment. Dr. Pack worked extensively in the initial development of PackBot™ systems in the DARPA Tactical Mobile Robotics program. He was the principal investigator for NEOReach, an advanced mobile manipulation system. Prior to iRobot, Dr. Pack was the lab manager of the Intelligent Robotics Lab at Vanderbilt University.
Computer Science-Case Western Reserve University, is the Principal Investigator for the Wayfarer Project, $1.3 million effort funded by the U.S. Army Tank automotive and Armaments Research, Development, and Engineering Center (TARDEC). His focus is to develop autonomous urban reconnaissance capabilities for man-portable robots such as the iRobot PackBots. Dr. Yamauchi has extensive experience in development of Situational / Context Specific Behavioral Control. Prior to iRobot, he conducted research at the Naval Research Laboratory in Washington, DC and developed frontier-based exploration, a technique that enables robots to autonomously explore and map unknown environments. With NASA’s Jet Propulsion Laboratory, he developed a reactive outdoor navigation system for the Rocky III Mars rover prototype. At NASA’s Kennedy Space Center, he developed the autonomous control system for a robot arm prototype designed to inspect the radiator panels on the shuttle orbiter.
iRobot is the industry leader in the innovation and fielding of military robots and robotic vehicles. With over 500 military robots deployed to combat forces in Iraq and Afghanistan, plus groundbreaking research experience in autonomy, localization and mapping, and a solid systems integration portfolio, iRobot has developed a successful methodology for designing and building autonomous robotic vehicles for use in real world conditions.
R-Gator is a radio controlled robotic vehicle with semiautonomous behaviors incorporated into a standard COTS vehicle capable of hauling significant payloads. R-Gator is currently under evaluation by SPAWAR. Relevancy to the Urban Challenge includes checkpoint navigation; context-dependent speed control; safe following distances; navigation without reliance on GPS; moving/fixed obstacle avoidance; traversing road-related obstacles including dirt and potted roads.
Sentinel employs multiple semi-autonomous UGVs for primary reconnaissance tasks – dispersing into an unknown area. Performs coordinated mapping of any enclosed space, such as the interior of a building, or the tunnel network of a cave or man-made underground complex. Relevancy to the Urban Challenge includes checkpoint navigation, synthesizing input sensor data into dynamically drafted maps and navigation independent of GPS data.
Wayfarer builds on capabilities established under Sentinel. Focused on developing robust techniques for autonomous reconnaissance in cluttered urban environments. Relevancy to the Urban Challenge includes checkpoint navigation; creating dynamic road and terrain maps; using acquired map data for route planning; navigation with/without GPS data (depending on availability).