基于信息增益的SLAM图精简

doi:10.13973/j.cnki.robot.2014.0527

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引用本文:
程见童, 江振宇, 张银辉, 张为华. 基于信息增益的SLAM图精简[J]. 机器人, 2014, 36(5): 527-534.DOI: 10.13973/j.cnki.robot.2014.0527. CHENG Jiantong, JIANG Zhenyu, ZHANG Yinhui, ZHANG Weihua. Information Gain-based SLAM Graph Pruning. ROBOT, 2014, 36(5): 527-534. DOI: 10.13973/j.cnki.robot.2014.0527.

摘要

针对基于图论的同时定位与制图中，非线性约束方程组维数随机器人运行距离和时间的增加而不断增大的问题，提出一种基于信息增益的图精简算法．该算法通过评估精简前后特征点信息矩阵相对变化，删除观测信息量小于给定阈值的机器人位姿及相应的观测，达到显著简化优化问题的目的．根据测量球形协方差矩阵假设，给出了信息增益的精确和近似计算方法．通过恢复性图剪枝方法，确保图精简过程中的连通性．蒙特卡洛仿真和开源实验数据计算结果表明，在不引入明显的优化误差前提下，该方法可实现位姿和特征点90%的精简，显著提高图优化效率．

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关键词 ：同时定位与制图, 信息增益, 图优化, 图精简

Abstract：

In graph-based simultaneous localization and mapping, the dimension of nonlinear constraint equations increases linearly with the distance and duration of robots motion. An efficient approach based on information gain is proposed to prune the graph. By evaluating the relative variation of features' information matrices before and after the pruning, any observation information below the given threshold of the robot pose is pruned, as well as corresponding observations, so that the complexity of SLAM optimization problem is simplified significantly. Exact and approximate computation methods of information gain are provided, according to the assumption of spherical covariance of measurements. The connectivity of the pruned graph is kept using the recovered pruning method. Experimental results based on Monte Carlo simulation and open-source environment dataset show that: around 90% of poses and features are pruned, on the premise that the optimization errors are not introduced apparently. The optimization efficiency is raised greatly.

Key words： simultaneous localization and mapping information gain graph optimization graph pruning

收稿日期: 2014-03-20 录用日期: 2014-06-27

TP391

基金资助:

国家自然科学基金资助项目（11102229）

通讯作者: 程见童，chengjiantong@gmail.com E-mail: chengjiantong@gmail.com

作者简介: 程见童（1987-），博士生．研究领域：组合导航，同时定位与制图，室内定位．
江振宇（1978-），男，博士，副教授，硕士生导师．研究领域：飞行器设计与仿真．
张银辉（1986-），男，博士生．研究领域：控制系统与半实物仿真．

链接本文:

https://robot.sia.cn/CN/10.13973/j.cnki.robot.2014.0527 或 https://robot.sia.cn/CN/Y2014/V36/I5/527

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