Fast vision-guided mobile robot navigation using model-based reasoning and prediction of uncertainties

The model-based vision system described in this thesis allows a mobile robot to navigate indoors at an average speed of 8 meters/minute using ordinary laboratory computing hardware of approximately 16 MIPS power. The navigation capabilities of the robot are not impaired by the presence of the stationary or moving obstacles. The vision system maintains a model of uncertainty and keeps track of the growth of uncertainty as the robot travels towards the goal position. The estimates of uncertainty are then used to predict bounds on the locations and orientations of landmarks expected to be seen in a monocular image. This greatly reduces the search for establishing correspondence between the features visible in the image and the landmarks. Given a sequence of image features and a sequence of landmarks derived from a geometric model of the environment, a special aspect of our vision system is the sequential reduction in the uncertainty as each image feature is matched successfully with a landmark, allowing subsequent features to be matched more easily, this being a natural byproduct of the manner in which we use Kalman-filter based updating. Strategies for path planning, path replanning and perception planning are introduced for the robot to navigate in the presence of obstacles. Finally, experimental results are presented.