An approach to solving large-scale SLAM problems with a small memory footprint
An approach to solving large-scale SLAM problems with a small memory footprint
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DOI：
10.1109/icra.2014.6907384
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发表时间：
2014-05
期刊：
影响因子：
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通讯作者：
Benjamin Suger;Gian Diego Tipaldi;Luciano Spinello;Wolfram Burgard
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文献类型：
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作者：
Benjamin Suger;Gian Diego Tipaldi;Luciano Spinello;Wolfram Burgard
过去，已经开发了基于解决非线性优化问题的SLAM问题的高效解决方案。但是，大多数方法的重点是运行时和准确性，而不是记忆消耗，这在必须解决大规模的大规模大规模问题时尤其重要。在本文中，我们从记忆消耗的角度考虑了大满贯问题，并提出了一种新颖的近似方法，以减少记忆消耗。我们的方法基于层次分解，该分解由尺寸有限的小型s子组成。我们对合成和公开数据集进行了广泛的实验。结果表明，与最先进的精确求解器相比，完整映射的表示所需的代表不仅仅是可用的主内存，我们的方法，将内存消耗和运行时降低到一个因素2仍提供高度准确的地图。
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.