Abstract
One of the key issues in Gaussian SLAM is to calculate nonlinear transition density of Gaussian prior, i.e. to calculate Gaussian Weight Integral (GWI) whose integrand is with the form nonlinear function × Gaussian prior density. Up to now, some GWI solutions have been applied in SLAM (e.g. linearization, unscented transform and cubature rule), and different SLAM algorithms were derived based on theirs GWI solutions. While, how to select suitable GWI solution for SLAM is still lack of theoretical analysis. In this paper, we proposed an optimal proposal FastSLAM algorithm with suitable GWI solutions. The main contributions of this work lies that: (1) an unified FastSLAM framework with optimal proposal distribution is summarized; (2) a SLAM dimensionality based GWI solution selection criterion is designed; (3) we propose a new SLAM algorithm. The performance of the proposed SLAM is investigated and compared with the FastSLAM2.0 and UFastSLAM using simulations and our opinion is confirmed by the results.
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Li, Q., Song, Y., Hou, Z., Zhu, B. (2013). Optimal Proposal Distribution FastSLAM with Suitable Gaussian Weighted Integral Solutions. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42054-2_38
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DOI: https://doi.org/10.1007/978-3-642-42054-2_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42053-5
Online ISBN: 978-3-642-42054-2
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