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.

Augmented reality (AR) technology is mature for creating learning experiences for K-12 (pre-school, grade school, and high school) educational settings. We reviewed the applications intended to complement traditional curriculum materials for K-12. We found 87 research articles on augmented reality learning experiences (ARLEs) in the IEEE Xplore Digital Library and other learning technology publications. Forty-three of these articles conducted user studies, and seven allowed the computation of an effect size to the performance of students in a test. In our meta-analysis, research show that ARLEs achieved a widely variable effect on student performance from a small negative effect to a large effect, with a mean effect size of 0.56 or moderate effect. To complement this finding, we performed a qualitative analysis on the design aspects for ARLEs: display hardware, software libraries, content authoring solutions, and evaluation techniques. We explain that AR incur three inherent advantages: real world annotation, contextual visualization, and vision-haptic visualization. We illustrate these advantages through the exemplifying prototypes, and ground these advantages to multimedia learning theory, experiential learning theory, and animate vision theory. Insights from this review are aimed to inform the design of future ARLEs.

Augmented reality (AR) has the potential to create compelling learning experiences. However, there are few research works exploring the design and evaluation of AR for educational settings. In our research, we treat AR as a type of multimedia that is situated in authentic environments and apply multimedia learning theory as a framework for developing our educational applications. We share our experiences in developing a handheld AR system and one specific use case, namely, situated vocabulary learning. Results of our evaluations show that we are able to create AR applications with good system usability. More importantly, our preliminary evaluations show that AR may lead to better retention of words and improve student attention and satisfaction.

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Usability evaluations are important to improving handheld augmented reality (HAR) systems. However, no standard questionnaire considers perceptual and ergonomic issues found in HAR. The authors performed a systematic literature review to enumerate these issues. Based on these issues, they created a HAR usability scale that consists of comprehensibility and manipulability scales. These scales measure general system usability, ease of understanding the information presented, and ease of handling the device. The questionnaires' validity and reliability were evaluated in four experiments, and the results show that the questionnaires consistently correlate with other subjective and objective measures of usability. The questionnaires also have good reliability based on the Cronbach's alpha. Researchers and professionals can directly use these questionnaires to evaluate their own HAR applications or modify them with the insights presented in this article.