机器人Issue(5)：527-534,8.DOI:10.13973/j.cnki.robot.2014.0527
基于信息增益的SLAM图精简
Information Gain-based SLAM Graph Pruning
摘要
Abstract
In graph-based simultaneous localization and mapping, the dimension of nonlinear constraint equations increas-es 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 ob-servation 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.关键词
同时定位与制图/信息增益/图优化/图精简Key words
simultaneous localization and mapping/information gain/graph optimization/graph pruning分类
信息技术与安全科学引用本文复制引用
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,(5):527-534,8.基金项目
国家自然科学基金资助项目（11102229）． （）
