Unscented Transformation of Vehicle States in SLAM

In this article we propose an algorithm to reduce the effects caused by linearization in the typical EKF approach to SLAM. The technique consists in computing the vehicle prior using an Unscented Transformation. The UT allows a better nonlinear mean and variance estimation than the EKF. There is no...

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Bibliographic Details
Main Authors: Andrade-Cetto, J., Vidal-Calleja, T., Sanfeliu, A.
Format: Conference Proceeding
Language:English
Subjects:
Computational complexity
Gaussian noise
Linearity
Particle filters
Probability density function
Robots
Simultaneous localization and mapping
State estimation
Technological innovation
Vehicles
Online Access:Request full text
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Summary:In this article we propose an algorithm to reduce the effects caused by linearization in the typical EKF approach to SLAM. The technique consists in computing the vehicle prior using an Unscented Transformation. The UT allows a better nonlinear mean and variance estimation than the EKF. There is no need however in using the UT for the entire vehicle-map state, given the linearity in the map part of the model. By applying the UT only to the vehicle states we get more accurate covariance estimates. The a posteriori estimation is made using a fully observable EKF step, thus preserving the same computational complexity as the EKF with sequential innovation. Experiments over a standard SLAM data set show the behavior of the algorithm.
ISSN:1050-4729
2577-087X
DOI:10.1109/ROBOT.2005.1570139