G2o: A general framework for graph optimization
G2o: A general framework for graph optimization
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DOI：
10.1109/icra.2011.5979949
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发表时间：
2011-05
期刊：
影响因子：
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通讯作者：
R. Kümmerle;G. Grisetti;H. Strasdat;K. Konolige;Wolfram Burgard
中科院分区：
文献类型：
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作者：
R. Kümmerle;G. Grisetti;H. Strasdat;K. Konolige;Wolfram Burgard
机器人技术和计算机视觉中的许多流行问题，包括各种类型的同时定位和映射（SLAM）或束调整（BA），都可以描述为误差函数的最小二乘优化，该误差函数可以用图表示。本文描述了这类问题的一般结构，并提出了g20，一个用于优化基于图的非线性误差函数的开源c++框架。我们的系统被设计成很容易扩展到广泛的问题，一个新问题通常可以在几行代码中指定。当前的实现为SLAM和BA的几个变体提供了解决方案。我们提供对广泛的现实世界和模拟数据集的评估。结果表明，虽然g20是通用的，但它提供的性能可与针对特定问题的最先进方法的实现相媲美。
Many popular problems in robotics and computer vision including various types of simultaneous localization and mapping (SLAM) or bundle adjustment (BA) can be phrased as least squares optimization of an error function that can be represented by a graph. This paper describes the general structure of such problems and presents g2o, an open-source C++ framework for optimizing graph-based nonlinear error functions. Our system has been designed to be easily extensible to a wide range of problems and a new problem typically can be specified in a few lines of code. The current implementation provides solutions to several variants of SLAM and BA. We provide evaluations on a wide range of real-world and simulated datasets. The results demonstrate that while being general g2o offers a performance comparable to implementations of state-of-the-art approaches for the specific problems.