Decomposition of line segments into corner and statistical grown line features in an EKF-SLAM framework
Robots are emerging from industrial plants toward every people's daily life. Thus, navigation in and understanding of human related environments becomes a prerequisite for the systems of tomorrow. Most such environments can be efficiently described using line segments. However, incorporation of extent information is often difficult, as line segments are seldom observed completely and erroneous data-association may corrupt the information associated to a certain segment. To reduce such problems this paper proposes a statistically driven description of line segments. The corresponding parameters are decomposed into line and corner features, which are separately tracked through an Extended Kalman Filter (EKF). Information about the extent of the lines is encoded statistically. Therefore, we use a method to recursively incorporate the information gained through a time-series of measurements. Thus, the covariance matrix belonging to a line segment grows as new regions of the corresponding line are discovered. Experimental results obtained by implementation on the mobile platform ITrike show the validity of our algorithm.