Extending the limits of feature-based SLAM with B-splines

Authors:
Luis Pedraza;Diego Rodriguez-Losada;Fernando Matía;Gamini Dissanayake;Jaime Valls Miró
Affiliations:
Intelligent Control Group, Universidad Politécnica de Madrid, Madrid, Spain;Intelligent Control Group, Universidad Politécnica de Madrid, Madrid, Spain;Intelligent Control Group, Universidad Politécnica de Madrid, Madrid, Spain;School of Electrical, Mechanical, and Mechatronic Systems, University of Technology, Sydney, N.S.W., Australia;School of Electrical, Mechanical, and Mechatronic Systems, University of Technology, Sydney, N.S.W., Australia
Venue:
IEEE Transactions on Robotics
Year:
2009

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Abstract

This paper describes a simultaneous localization and mapping (SLAM) algorithm for use in unstructured environments that is effective regardless of the geometric complexity of the environment. Features are described using B-splines as modeling tool, and the set of control points defining their shape is used to form a complete and compact description of the environment, thus making it feasible to use an extended Kalman-filter (EKF) based SLAM algorithm. This method is the first known EKF-SLAM implementation capable of describing general free-form features in a parametric manner. Efficient strategies for computing the relevant Jacobians, perform data association, initialization, and map enlargement are presented. The algorithms are evaluated for accuracy and consistency using computer simulations, and for effectiveness using experimental data gathered from different real environments.