| 引用本文: | 王忠立,赵杰,蔡鹤皋.大规模环境下基于图优化SLAM的后端优化方法[J].哈尔滨工业大学学报,2015,47(7):20.DOI:10.11918/j.issn.0367-6234.2015.07.002 |
| WANG Zhongli,ZHAO Jie,CAI Hegao.A survey of back-end optimization method for graph-based SLAM under large-scale environment[J].Journal of Harbin Institute of Technology,2015,47(7):20.DOI:10.11918/j.issn.0367-6234.2015.07.002 |
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| 摘要: |
| 在总结图优化同步定位和建图(SLAM)的前端图构建方法的基础上,对现有的后端图优化方法进行分析,介绍了最小二乘法、随机梯度下降法、松弛法、流形优化及其相关文献．讨论基于χ2误差和基于均方差的地图创建的评价方法,对图优化方法的发展趋势进行了展望． |
| 关键词: 移动机器人 大规模环境 同步定位与建图 图建模 图优化 |
| DOI：10.11918/j.issn.0367-6234.2015.07.002 |
| 分类号:TP242.6 |
| 基金项目:国家自然科学基金(61075079). |
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| A survey of back-end optimization method for graph-based SLAM under large-scale environment |
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WANG Zhongli1,2, ZHAO Jie2, CAI Hegao2
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(1.School of Electronic and Information Engineering,Beijing Jiaotong University, 100044 Beijing, China; 2.State Key Laboratory of Robotics and System(Harbin Institute of Technology), 150080 Harbin, China)
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| Abstract: |
| Graph optimization-based SLAM is the main method under large-scale environment. The framework of this method is composed of two parts, front-end and back-end. Be a continuation paper of our previous one, the four main back-end optimization approaches, which include least square, stochastic gradient descent, relaxation, manifold optimization, and the correspondent literatures are introduced, and two map evaluation methods are presented, that is χ2 error based and MSE error based. The trends of graph optimization-based SLAM method are predicted. |
| Key words: mobile robot large-scale environment simultaneous localization and mapping (SLAM) graph modeling graph optimization |
