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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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