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A visual navigation system is described which uses texture mapped primitives to represent clusters of objects to maintain high and approximately constant frame rates. In cases where there are more unoccluded primitives inside the viewing frustum than can be drawn in real-time on the workstation

1 column Abstract — We present and validate a framework for visual navigation with obstacle avoidance. The approach was originally designed in [1], but major improvements and real outdoor experiments are added here. Visual navigation consists of following a path, represented as an ordered set

A prototypical example of the operational dimensions of visual information communication is the use of schematic maps for visual navigation. The implementation of maps on location-sensitive or handheld devices has changed the preliminaries of common mapping techniques. By an analysis of selected

. This paper describes the use of appearance based vision for defining visual processes for navigation. A visual processes which transform images to commands and events. A family of visual processes are defined by associating the appearance of a scene from a given viewpoint with the simple

. This paper considers a method for the visual-based navigation of a mobile robot in an indoor environment. The sole sensor used is an omni-directional camera. Navigation is achieved by combining appearance based methods and visual servoing. A database of omni-directional images is obtained during

. We address the problem of visual-based indoors navigation based on a single camera that provides the required visual feedback information. The usual approach relies on a map to relocate the robot with respect to the environment. Once the robot position and orientation are known, a suitable

to those models, the navigation strategies presented in this paper are not pre-defined but learned in interaction with the environment. The preconditions for the ability to learn are kept as minimal as possible. The learned homing strategies have been tested in both simulation and on a mobile robot.

Visual processing is very important for robot navigation. It has been demonstrated that many complex operations, which deserve an intelligent behaviour, can be performed relying only on reflexes to visual stimuli. In this framework the detection of corridors of free space along the robot trajectory