Indoor navigation of a non-holonomic mobile robot using a visual memory
When navigating in an unknown environment for the first time, a natural behavior consists on memorizing some key views along the performed path, in order to use these references as checkpoints for a future navigation mission. The navigation framework for wheeled mobile robots presented in this paper is based on this assumption. During a human-guided learning step, the robot performs paths which are sampled and stored as a set of ordered key images, acquired by an embedded camera. The set of these obtained visual paths is topologically organized and provides a visual memory of the environment. Given an image of one of the visual paths as a target, the robot navigation mission is defined as a concatenation of visual path subsets, called visual route. When running autonomously, the robot is controlled by a visual servoing law adapted to its nonholonomic constraint. Based on the regulation of successive homographies, this control guides the robot along the reference visual route without explicitly planning any trajectory. The proposed framework has been designed for the entire class of central catadioptric cameras (including conventional cameras). It has been validated onto two architectures. In the first one, algorithms have been implemented onto a dedicated hardware and the robot is equipped with a standard perspective camera. In the second one, they have been implemented on a standard PC and an omnidirectional camera is considered.
mobile robot visual navigation central camera visual-based control