吕瑞, 陈龙, 翁雪, 何金城. 利用先验点图模型的SLAM后端优化算法[J]. 武汉大学学报 ( 信息科学版), 2014, 39(6): 745-749. DOI: 10.13203/j.whugis20140049
引用本文: 吕瑞, 陈龙, 翁雪, 何金城. 利用先验点图模型的SLAM后端优化算法[J]. 武汉大学学报 ( 信息科学版), 2014, 39(6): 745-749. DOI: 10.13203/j.whugis20140049
LV Rui, CHEN Long, WENG Xue, HE Jincheng. A Back-End Optimization Algorithm of SLAM Based onGraph Model with Prior Points[J]. Geomatics and Information Science of Wuhan University, 2014, 39(6): 745-749. DOI: 10.13203/j.whugis20140049
Citation: LV Rui, CHEN Long, WENG Xue, HE Jincheng. A Back-End Optimization Algorithm of SLAM Based onGraph Model with Prior Points[J]. Geomatics and Information Science of Wuhan University, 2014, 39(6): 745-749. DOI: 10.13203/j.whugis20140049

利用先验点图模型的SLAM后端优化算法

A Back-End Optimization Algorithm of SLAM Based onGraph Model with Prior Points

  • 摘要: 目的 目前基于因子图的后端优化算法具有优越性。在因子图中,节点代表姿态,节点之间的边代表里程信息和封闭循环约束。由于因子图并未描述每个节点精度的差异,导致整体定位精度仍有提高的空间。针对这个问题,提出了一种基于先验点图模型的后端优化算法,依据前端提供节点精度的差异,在因子图中引入高精度点,然后采用改进的Levenberg算法进行全局优化,从而实现在结合原有概率约束的基础上,利用少量高精度点牵引其他点向真实值靠近,完成更为精准的自身定位。并在公开数据集上进行了实验,结果证明,本文提出的算法增强了前后端的关联,提高了定位精度。

     

    Abstract: Objective Simultaneous localization and mapping(SLAM)is a hot issue in the field of robotics,theproblem consists of two parts,front-end perception and back-end optimization.At present,the back-end optimization algorithm based on factor graph works well.In the factor graph,nodes represent po-ses,the edges between nodes represents the range information and closed loop constraint.Since theaccuracy differences of nodes are not described in factor graph,the global positioning accuracy can beimproved no further.To solve this problem,we propose a back-end optimization algorithm based ongraph model with prior points which are introduced from the front-end that updates the graph modelby fixing high-precision poses during optimization.The back-end can thereby use these fixed high-pre-cision poses to drag low-precision poses closer to the ground truth and increase overall accuracy.Wedemonstrate the approach and present results on public datasets.The experimental results show thatthe maps acquired with our method show increased global precision.

     

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