SLAM is an abbreviation for simultaneous localization and mapping, which is a technique for estimating sensor motion and reconstructing structure in an unknown environment. Especially, Simultaneous Localization and Mapping (SLAM) using cameras is referred to as visual SLAM (vSLAM) because it is based on visual information only. vSLAM can be used as a fundamental technology for various types of applications and has been discussed in the field of computer vision, augmented reality, and robotics in the literature. This paper aims to categorize and summarize recent vSLAM algorithms proposed in different research communities from both technical and historical points of views. Especially, we focus on vSLAM algorithms proposed mainly from 2010 to 2016 because major advance occurred in that period. The technical categories are summarized as follows: feature-based, direct, and RGB-D camera-based approaches.

中文翻译：

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Visual SLAM算法：2010年至2016年的调查

SLAM是同时定位和映射的缩写，是一种在未知环境中估计传感器运动并重建结构的技术。特别是，使用摄像头的同时定位和映射（SLAM）被称为视觉SLAM（vSLAM），因为它仅基于视觉信息。vSLAM可以用作各种类型应用程序的基本技术，并且已经在计算机视觉，增强现实和机器人技术领域进行了讨论。本文旨在从技术和历史角度对不同研究社区中提出的最新vSLAM算法进行分类和总结。特别是，我们专注于主要在2010年至2016年提出的vSLAM算法，因为在此期间出现了重大进展。技术类别总结如下：