vSLAM: vision-based SLAM for autonomous vehicle navigation

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.