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 are used to control the new action of the robot. Use of RPT can achieve efficient 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 confirm the effectiveness of proposed method.