Maximally informative statistics for localization and mapping
- 25 June 2003
- proceedings article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2, 1824-1829
- https://doi.org/10.1109/robot.2002.1014806
Abstract
This paper presents an algorithm for simultane- ous localization and mapping for a mobile robot using monocular vision and odometry. The approach uses Variable State Dimension Filtering (VSDF) flame- work to combine aspects of extended Kalman filtering (EKF) and nonlinear batch optimization. This pa- per describes two primary improvements to the VSDF. The first is to use the maximally informative statis- tics criterion to derive an interpolation scheme for lin- earization in recursive filtering. The interpolation is based on fitting a set of deterministic samples rather than using analytic Jacobians. The second improve- ment is to replace the inverse covariance matrix in the VSDF with its Cholesky factor to improve the compu- tational complexity. Results of applying the filter to the localization and mapping are presented.Keywords
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