知识与财富的链接
分析总结了基于图优化同步定位和地图构建(SLAM)前端图构建过程的各种方法.对现有SLAM研究方法进行分类,指出基于Kalman滤波器、粒子滤波器、图优化方法的优缺点;重点介绍SLAM问题的3种图建模方法,即动态贝叶斯网络的图建模方法、基于因子图的建模方法、基于Markov随机场的建模方法;对图优化SLAM方法前端图构建的核心环节——帧间数据关联和环形闭合检测方法进行了分析;讨论了特征提取、特征匹配、运动估计、环形闭合检测等方面的最新研究成果.
The existing graph-construction methods for graph optimization-based SLAM are summarized. The SLAM methods can be divided into three main classes, Kalman filter-based, partical filter-based and graph optimization-based, and the advantages and disadvantages of each class are overviewed. Moreover, there are mainly three graph modeling methods for the graph optimization-based SLAM problem, namely dynamic Bayesian network (DBN)-based model, factor graph-based model and Markov random field-based model. The key techniques of the front-end stage in graph optimization-based SLAM method, which mainly include data association between consecutive frame and loop closure detection, are discussed. Some newest research achievements on feature extraction, matching method, motion estimation, loop closure detection are introduced.
