An approach to solving large-scale SLAM problems with a small memory footprint | IEEE Conference Publication | IEEE Xplore

An approach to solving large-scale SLAM problems with a small memory footprint


Abstract:

In the past, highly effective solutions to the SLAM problem based on solving nonlinear optimization problems have been developed. However, most approaches put their major...Show More

Abstract:

In the past, highly effective solutions to the SLAM problem based on solving nonlinear optimization problems have been developed. However, most approaches put their major focus on runtime and accuracy rather than on memory consumption, which becomes especially relevant when large-scale SLAM problems have to be solved. In this paper, we consider the SLAM problem from the point of view of memory consumption and present a novel approximate approach to SLAM with low memory consumption. Our approach achieves this based on a hierarchical decomposition consisting of small submaps with limited size. We perform extensive experiments on synthetic and publicly available datasets. The results demonstrate that in situations in which the representation of the complete map requires more than the available main memory, our approach, in comparison to state-of-the-art exact solvers, reduces the memory consumption and the runtime up to a factor of 2 while still providing highly accurate maps.
Date of Conference: 31 May 2014 - 07 June 2014
Date Added to IEEE Xplore: 29 September 2014
Electronic ISBN:978-1-4799-3685-4
Print ISSN: 1050-4729
Conference Location: Hong Kong, China

References

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