Publikation
How far is SLAM from a linear least squares problem?
S. Huang; Y. Lai; Udo Frese; G. Dissanayake
In: Proceedings of the International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-2010), September 18-22, Taipei, Taiwan, Province of China, IEEE, 2010.
Zusammenfassung
Most people believe SLAM is a complex nonlin-
ear estimation/optimization problem. However, recent research
shows that some simple iterative methods based on linearization
can sometimes provide surprisingly good solutions to SLAM
without being trapped into a local minimum. This demonstrates
that hidden structure exists in the SLAM problem that is yet to
be understood. In this paper, we first analyze how far SLAM
is from a convex optimization problem. Then we show that
by properly choosing the state vector, SLAM problem can be
formulated as a nonlinear least squares problem with many
quadratic terms in the objective function, thus it is clearer how
far SLAM is from a linear least squares problem. Furthermore,
we explain that how the map joining approaches reduce the
nonlinearity/nonconvexity of the SLAM problem.