视觉SLAM方法综述

王朋; 郝伟龙; 倪翠; 张广渊; 巩慧

doi:10.13700/j.bh.1001-5965.2022.0376

视觉SLAM方法综述

doi: 10.13700/j.bh.1001-5965.2022.0376

王朋^{1, 2},
郝伟龙¹,
倪翠^1, ,,
张广渊¹,
巩慧¹

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

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

详细信息

通讯作者:
E-mail：emilync@126.com

中图分类号: TP391.4
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出版历程
- 收稿日期: 2022-05-18
- 录用日期: 2022-06-23
- 网络出版日期: 2022-10-10
- 整期出版日期: 2024-02-27

An overview of visual SLAM methods

WANG Peng^{1, 2},
HAO Weilong¹,
NI Cui^{1
, ,},
ZHANG Guangyuan¹,
GONG Hui¹

1.
School of Information Science and Electrical Engineering，Shandong Jiaotong University，Jinan 250357，China
2.
Institute of Automation，Shandong Academy of Sciences，Jinan 250013，China

Funds: China Postdoctoral Science Foundation (2021M702030); Science and Technology Programe of Transportation Department of Shandong Province (2021B120)

More Information

Corresponding author: E-mail：emilync@126.com

摘要

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。
- 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
Abstract:
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.
- visual simultaneous localization and mapping /
- deep learning /
- feature detection /
- pose estimation /
- loop closure detection

HTML全文

图 1 视觉SLAM方法流程

Figure 1. Flow of visual SLAM methods

下载: 全尺寸图片幻灯片

图 2 PTAM框架

Figure 2. PTAM framework

下载: 全尺寸图片幻灯片

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