Simultaneous Localization and Mapping is now widely adopted by many applications, and researchers have produced very dense literature on this topic. With the advent of smart devices, embedding cameras, inertial measurement units, visual SLAM (vSLAM), and visual-inertial SLAM (viSLAM) are enabling novel general public applications. In this context, this paper conducts a review of popular SLAM approaches with a focus on vSLAM/viSLAM, both at fundamental and experimental levels. It starts with a structured overview of existing vSLAM and viSLAM designs and continues with a new classification of a dozen main state-of-the-art methods. A chronological survey of viSLAM’s development highlights the historical milestones and presents more recent methods into a classification. Finally, the performance of vSLAM is experimentally assessed for the use case of pedestrian pose estimation with a handheld device in urban environments. The performance of five open-source methods Vins-Mono, ROVIO, ORB-SLAM2, DSO, and LSD-SLAM is compared using the EuRoC MAV dataset and a new visual-inertial dataset corresponding to urban pedestrian navigation. A detailed analysis of the computation results identifies the strengths and weaknesses for each method. Globally, ORB-SLAM2 appears to be the most promising algorithm to address the challenges of urban pedestrian navigation, tested with two datasets.

中文翻译：

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视觉和视觉惯性SLAM：最新技术，分类和实验基准

现在，同时定位和制图已被许多应用程序广泛采用，研究人员已针对此主题撰写了非常密集的文献。随着智能设备的出现，嵌入式相机，惯性测量单元，视觉SLAM（vSLAM）和视觉惯性SLAM（viSLAM）使得新型的公共应用成为可能。在这种情况下，本文从基础和实验两个层面对流行的SLAM方法进行了综述，重点是vSLAM / viSLAM。它从对现有vSLAM和viSLAM设计的结构化概述开始，然后继续对十几种主要的最新方法进行新的分类。按时间顺序对viSLAM的发展进行了调查，突显了历史里程碑，并提出了更多分类的最新方法。最后，针对城市环境中手持设备行人姿势估计的用例，通过实验评估了vSLAM的性能。使用EuRoC MAV数据集和对应于城市行人导航的新视觉惯性数据集，比较了五个开源方法Vins-Mono，ROVIO，ORB-SLAM2，DSO和LSD-SLAM的性能。对计算结果的详细分析确定了每种方法的优缺点。在全球范围内，ORB-SLAM2似乎是应对城市行人导航挑战的最有前途的算法，并通过两个数据集进行了测试。使用EuRoC MAV数据集和对应于城市行人导航的新视觉惯性数据集，对LSD-SLAM进行了比较。对计算结果的详细分析确定了每种方法的优缺点。在全球范围内，ORB-SLAM2似乎是应对城市行人导航挑战的最有前途的算法，并通过两个数据集进行了测试。使用EuRoC MAV数据集和对应于城市行人导航的新视觉惯性数据集，对LSD-SLAM进行了比较。对计算结果的详细分析确定了每种方法的优缺点。在全球范围内，ORB-SLAM2似乎是应对城市行人导航挑战的最有前途的算法，并通过两个数据集进行了测试。