EDUCATION Ph.D. (9/98) Robotics, Carnegie Mellon University. Project: Automated Highway Systems, Navlab Thesis topic: A High-Performance Stereo Vision System for Obstacle Detection. Advisor: Dr. Charles Thorpe M.S. (5/94) Robotics, Carnegie Mellon University. Project: Unmanned Ground Vehicle (UGV), Navlab Advisor: Dr. Charles Thorpe B.S. (5/91) Applied Mathematics (Computer Science), Carnegie Mellon University B.S. (5/91) Physics, Carnegie Mellon University (with honors)

Advisors: Vitor Santos, Angel D. Sappa. Date and location of PhD thesis defense: 21 February 2013, University of Aveiro (Portugal)

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Carnegie Mellon Navlab Vision System

Automatic Information and Safety Systems for Driving Assistance

A mobile robot, Mover O, has been developed to navigate indoor environments. The robustness of the low-level image processing and the real-time performance of the visual guiding system were confirmed to be feasible for use in the real world. The robot navigated successfully in an environment consisting of rooms and corridors without extremely complex structures and moving obstacles. When the robot moved along continuous landmarks and the directions of the landmarks did not change drastically, the robot was able to run at a top speed of 2.5 km/h. The robot was able to find dispersed landmarks placed at intersections of corridors, and followed the instructed courses in the building. >

Development of an indoor mobile robot 'Mover O'

This paper presents the development of a visual guiding system for a mobile robot. Lower level functions detecting navigable areas under the disturbances caused by illuminating conditions a nd their implementation on the robot is described.

Technical Research Center (Yokohama)

The contribution of this research is the development of a security system the purpose of which is to assist the teleoperator of a mobile robot by providing him pertinent information about the robot working space. In this aim, the range image based perception system must be able to detect and localize, as fast as possible, obstacles (nontraversable regions) in the scene during robot's motion. In this paper we present an accelerated strategy of obstacle detection using the knowledge of objects contained in previous images and their predicted positions in the new image. An iterative procedure based on a Kalman filter is used to improve robot's state knowledge. Then, an image segmentation takes place according to security and emergency criteria. The range image is divided in priority areas to reduce obstacle research time. Some results obtained through experimentation of this method on synthetic images are developed in this paper.

Carnegie Mellon Navlab Vision System

Citations

Automatic Information and Safety Systems for Driving Assistance

Development of an indoor mobile robot 'Mover O'

Technical Research Center (Yokohama)

A range security system for vehicle navigation

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