视觉SLAM方法综述  

An overview of visual SLAM methods

作　　者：王朋[1,2] 郝伟龙 倪翠 张广渊[1] 巩慧[1] WANG Peng;HAO Weilong;NI Cui;ZHANG Guangyuan;GONG Hui(School of Information Science and Electrical Engineering,Shandong Jiaotong University,Jinan 250357,China;Institute of Automation,Shandong Academy of Sciences,Jinan 250013,China)

机构地区：[1]山东交通学院信息科学与电气工程学院,济南250357 [2]山东省科学院自动化研究所,济南250013

出　　处：《北京航空航天大学学报》2024年第2期359-367,共9页Journal of Beijing University of Aeronautics and Astronautics

基　　金：中国博士后科学基金(2021M702030);山东省交通运输厅科技计划(2021B120)。

摘　　要：实时定位与建图(SLAM)技术搭载特定传感器,使移动机器人在无任何环境先验条件下,在运动过程中自主建立环境模型来计算自身位姿,大幅提高其自主导航能力,以及对不同应用环境的适应性。视觉SLAM方法以相机作为外部传感器,通过采集周围环境信息来创建地图并实时估计机器人自身位姿。为此,介绍了具有代表性的经典视觉SLAM方法及与深度学习相结合的视觉SLAM方法,分析了视觉SLAM方法中采用的不同特征检测方法、后端优化、闭环检测,以及动态环境下视觉SLAM方法的应用,总结了视觉SLAM方法的问题,并探讨了视觉SLAM方法在未来的热点研究方向和发展前景。Simultaneous localization and mapping(SLAM)enables mobile robots to calculate their position and pose by independently building an environment model during movement without any environmental prior conditions by carrying specific sensors.It can greatly improve the autonomous navigation ability of mobile robots and their adaptability to different application environments,and contribute to the subsequent implementation of dynamic path planning,real-time obstacle avoidance and multi-robot collaboration.Visual SLAM refers to using the camera as an external sensor to collect ambient information to create a map and estimate the robot’s own position in real-time.The study describes and examines the various feature detection approaches,back-end optimization,loop closure detection,and the application of visual SLAM in a dynamic environment in addition to introducing the standard classical visual SLAM methods and the visual SLAM methods mixed with deep learning.This study addresses the current state-of-the-art in research and the potential growth of visual SLAM in the future before summarizing the issues with visual SLAM raised here.

关 键 词：视觉实时定位与建图 深度学习 特征检测 位姿估计 闭环检测 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]