摘要
为了对SLAM技术有更为全面的把握,在回顾过去三十年里视觉SLAM技术发展历程基础上,详细分析了视觉SLAM问题的本质与求解的复杂性。重点对在提高位姿估计精度、构建全局一致地图与提升算法求解效率上的最新研究成果进行了介绍,并对当前代表性的算法实现方案进行了分析与比较。针对未来大尺度环境、全生命周期应用需求,对现有算法框架的不足与最新研究趋势进行了归纳总结。最后,探讨了深度学习技术与视觉SLAM问题求解的关联性。
In order to give a comprehensive understanding of SLAM,this paper first gave an overview of the progress of visual SLAM community has made over the last 30 years in this survey.Then it presented the non-linearity of the mathematical model and computational complexity in visual SLAM algorithms.This paper mostly focused on the latest achievements and approached improving the accuracy of pose estimation,building a globally consistency representation of the environment and promoting computation efficiency.It also surveyed the failure modes of current visual SLAM algorithms for large scale,full lifecycle implementation and the different ways to address that.Finally,it discussed the potential connections between deep learning architectures and visual SLAM state estimations.
作者
王录涛
吴林峰
Wang Lutao;Wu Linfeng(School of Computer Science,Chengdu University of Information Technology,Chengdu 610225,China)
出处
《计算机应用研究》
CSCD
北大核心
2020年第1期9-15,共7页
Application Research of Computers
基金
四川省科技厅重点研发项目(18ZDYF3214)
成都市科技惠民技术研发项目(2016-HM01-00406-SF).
关键词
同步定位与构图
图优化
数据关联
稀疏化
深度学习
simultaneous localization and mapping(SLAM)
graph optimization
data association
sparsification
deep learning
