| Publication type: |
Article in Proceedings |
| Author: |
S. Huang, Y. Lai, U. Frese, G. Dissanayake |
| Title: |
How far is SLAM from a linear least squares problem? |
| Book / Collection title: |
Proceedings of the International Conference on Intelligent Robots and Systems |
| Year published: |
2010 |
| Abstract: |
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.
|
| PDF Version: |
http://www.informatik.uni-bremen.de/agebv/downloads/published/huang_iros_10.pdf |
| Keywords: |
SLAM |
| Status: |
Reviewed |
| Last updated: |
30. 07. 2010 |
|
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