Abstract
Most estimation problems in robotics are difficult because of (a) the nonlinearity in observation models; and (b) the lack of suitable probabilistic models for the process and observation noise. In this paper we develop a set-valued approach to estimation that overcomes both these limitations and illustrates the application to localization of multiple, mobile sensor platforms with range sensors.
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Stump, E., Grocholsky, B., Kumar, V. (2008). Extensive Representations and Algorithms for Nonlinear Filtering and Estimation. In: Akella, S., Amato, N.M., Huang, W.H., Mishra, B. (eds) Algorithmic Foundation of Robotics VII. Springer Tracts in Advanced Robotics, vol 47. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68405-3_11
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DOI: https://doi.org/10.1007/978-3-540-68405-3_11
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