Abstract:
This paper presents BS-SLAM, a simultaneous
localization and mapping algorithm for use in unstructured
environments that is effective regardless of whether features
correspond to simple geometric primitives such as lines or not.
The coordinates of the control points defining a set of B-splines
are used to form a complete and compact description of the
environment, thus making it feasible to use an extended Kalman
filter based SLAM algorithm. The proposed method is the first
known EKF-SLAM implementation capable of describing both
straight and curve features in a parametric way. Appropriate
observation equation that allows the exploitation of virtually
all observations from a range sensor such as the ubiquitous
laser range finder is developed. Efficient strategies for computing
the relevant Jacobians, perform data association, initialization
and expanding the map are presented. The effectiveness of the
algorithms is demonstrated using experimental data.
