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Content Provider | IEEE Xplore Digital Library |
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Author | Grisetti, G. Kummerle, R. Stachniss, C. Frese, U. Hertzberg, C. |
Copyright Year | 2010 |
Description | Author affiliation: University of Bremen, Germany (Frese, U.; Hertzberg, C.) || University of Freiburg, Germany (Grisetti, G.; Kummerle, R.; Stachniss, C.) |
Abstract | In this paper, we present a new hierarchical optimization solution to the graph-based simultaneous localization and mapping (SLAM) problem. During online mapping, the approach corrects only the coarse structure of the scene and not the overall map. In this way, only updates for the parts of the map that need to be considered for making data associations are carried out. The hierarchical approach provides accurate non-linear map estimates while being highly efficient. Our error minimization approach exploits the manifold structure of the underlying space. In this way, it avoids singularities in the state space parameterization. The overall approach is accurate, efficient, designed for online operation, overcomes singularities, provides a hierarchical representation, and outperforms a series of state-of-the-art methods. |
Starting Page | 273 |
Ending Page | 278 |
File Size | 1261507 |
Page Count | 6 |
File Format | |
ISBN | 9781424450381 |
ISSN | 10504729 |
DOI | 10.1109/ROBOT.2010.5509407 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2010-05-03 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Simultaneous localization and mapping Robots Newton method Least squares methods Recursive estimation State-space methods Testing Robotics and automation USA Councils Layout |
Content Type | Text |
Resource Type | Article |
Subject | Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software |
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