Incremental learning for vision-based navigation

In this paper, we explore the issue of incremental learning for autonomous navigation of a mobile robot. The autonomous navigation problem is regarded as a content-based retrieval problem where the robot learns the navigation experience using a hierarchical recursive partition tree (RPT). During real navigation, each time a new image is grabbed to retrieve the learned tree. The associated control signals of the retrieved image are used to control the next action of the robot. The use of RPT can achieve eecient retrieval. In the proposed incremental learning scheme, a new image with the associated control signals is learned or rejected according to whether its retrieved output control signals are within tolerance of the desired control signals of the input query image. We use the eigen subspace method for feature extraction in our incremental learning. The proposed algorithm has a real-time implementation for both learning and performance phases, experimental results are shown to connrm the eeectiveness of proposed method.