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一种混合视觉 SLAM 架构：基于关键帧的全局映射的局部滤波 SLAM。

A Hybrid Visual-Based SLAM Architecture: Local Filter-Based SLAM with KeyFrame-Based Global Mapping.

机构信息

Department of Computer Science (CUCEI), University of Guadalajara, Guadalajara 44430, Mexico.

Department of Automatic Control, Technical University of Catalonia UPC, 08034 Barcelona, Spain.

出版信息

Sensors (Basel). 2021 Dec 29;22(1):210. doi: 10.3390/s22010210.

DOI:10.3390/s22010210
PMID:35009753
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8749760/
Abstract

This work presents a hybrid visual-based SLAM architecture that aims to take advantage of the strengths of each of the two main methodologies currently available for implementing visual-based SLAM systems, while at the same time minimizing some of their drawbacks. The main idea is to implement a local SLAM process using a filter-based technique, and enable the tasks of building and maintaining a consistent global map of the environment, including the loop closure problem, to use the processes implemented using optimization-based techniques. Different variants of visual-based SLAM systems can be implemented using the proposed architecture. This work also presents the implementation case of a full monocular-based SLAM system for unmanned aerial vehicles that integrates additional sensory inputs. Experiments using real data obtained from the sensors of a quadrotor are presented to validate the feasibility of the proposed approach.

摘要

本工作提出了一种混合视觉 SLAM 架构，旨在利用当前用于实现视觉 SLAM 系统的两种主要方法的优势，同时最小化它们的一些缺点。主要思想是使用基于滤波器的技术实现局部 SLAM 过程，并使构建和维护环境一致的全局地图的任务，包括循环闭合问题，能够使用基于优化技术实现的过程。可以使用所提出的架构实现不同变体的基于视觉的 SLAM 系统。本工作还介绍了一种用于无人机的全单目视觉 SLAM 系统的实现案例，该系统集成了额外的传感器输入。使用从四旋翼飞行器传感器获得的真实数据进行了实验，以验证所提出方法的可行性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37bd/8749760/7726639abd09/sensors-22-00210-g012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37bd/8749760/18f8c7456b51/sensors-22-00210-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37bd/8749760/2e1f483f0d23/sensors-22-00210-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37bd/8749760/a91277dce567/sensors-22-00210-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37bd/8749760/d942a7652c3d/sensors-22-00210-g010.jpg
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MonoSLAM: real-time single camera SLAM.单目即时定位与地图构建（MonoSLAM）：实时单目相机即时定位与地图构建
IEEE Trans Pattern Anal Mach Intell. 2007 Jun;29(6):1052-67. doi: 10.1109/TPAMI.2007.1049.