Acquiring and maintaining abstract landmark chunks for cognitive robot navigation

In this paper, we discuss an important aspect of cognitive mobile robotics stemming from a new project in which an adaptive working memory is investigated for robot control and learning. Specifically, our approach is built on the premise that qualitative spatial reasoning is an appropriate framework to pose, learn, and solve navigational tasks. As such, the robot must be able to acquire and maintain landmarks in a form that facilitates learning and subsequent travel. Much research on landmark recognition has focused on either point landmarks or on landmark objects that come from segmentation and feature extraction. Here, we combine these approaches in the following sense. Potential landmark points are acquired in the point mode, but aggregations of them are utilized to represent "interesting" objects that can then be maintained throughout the path. In this paper, we investigate whether consistent aggregations can be maintained and thus serve as candidate chunks for the working memory system. The approach was tested on a video sequence of 1200 frames. Examples from this outdoor video are shown to corroborate the approach.