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Proceedings Paper

vSLAM: vision-based SLAM for autonomous vehicle navigation
Author(s): Luis Goncalves; Niklas Karlsson; Jim Ostrowski; Enrico Di Bernardo; Paolo Pirjanian
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Paper Abstract

Among the numerous challenges of building autonomous/unmanned vehicles is that of reliable and autonomous localization in an unknown environment. In this paper we present a system that can efficiently and autonomously solve the robotics 'SLAM' problem, where a robot placed in an unknown environment, simultaneously must localize itself and make a map of the environment. The system is vision-based, and makes use of Evolution Robotic's powerful object recognition technology. As the robot explores the environment, it is continuously performing four tasks, using information from acquired images and the drive system odometry. The robot: (1) recognizes previously created 3-D visual landmarks; (2) builds new 3-D visual landmarks; (3) updates the current estimate of its location, using the map; (4) updates the landmark map. In indoor environments, the system can build a map of a 5m by 5m area in approximately 20 minutes, and can localize itself with an accuracy of approximately 15 cm in position and 3 degrees in orientation relative to the global reference frame of the landmark map. The same system can be adapted for outdoor, vehicular use.

Paper Details

Date Published: 2 September 2004
PDF: 8 pages
Proc. SPIE 5422, Unmanned Ground Vehicle Technology VI, (2 September 2004); doi: 10.1117/12.543846
Show Author Affiliations
Luis Goncalves, Evolution Robotics, Inc. (United States)
Niklas Karlsson, Evolution Robotics, Inc. (United States)
Jim Ostrowski, Evolution Robotics, Inc. (United States)
Enrico Di Bernardo, Evolution Robotics, Inc. (United States)
Paolo Pirjanian, Evolution Robotics, Inc. (United States)

Published in SPIE Proceedings Vol. 5422:
Unmanned Ground Vehicle Technology VI
Grant R. Gerhart; Chuck M. Shoemaker; Douglas W. Gage, Editor(s)

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