Advanced liver surgery training in collaborative VR environments

Chheang, Vuthea; Schott, Danny; Saalfeld, Patrick; Vradelis, Lukas; Huber, Tobias; Huettl, Florentine; Lang, Hauke; Preim, Bernhard; Hansen, Christian

doi:10.1016/j.cag.2024.01.006

Cited by 4 publications

(1 citation statement)

References 63 publications

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“…The complex overlap of Simultaneous Localization and Mapping (SLAM) within the vast field of Extended Reality (XR), including Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), showcases the considerable progress made in different areas of science and technology [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. The integration of state-of-the-art SLAM algorithms in XR environments, combined with new computational models and immersive interaction techniques, reflects the significant changes seen in industries like robotics and autonomous vehicle navigation.…”

Section: Introductionmentioning

confidence: 99%

SLAM in XR: a bibliometric analysis of its application in virtual, augmented, and mixed reality

Obaideen,

AlShabi

2024

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI

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The growing sector of Extended Reality (XR), comprising Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), requires successful approaches for localization and mapping. Simultaneous Localization and Mapping (SLAM) has gained more importance in XR uses. This research conducts an in-depth bibliometric examination of the utilization of SLAM in XR settings, analyzing publications from the last ten years. We investigate the developing trends, fields of knowledge, prominent contributors, and important publications that have influenced this intersection. Our study indicates a notable increase in research on incorporating SLAM in XR, especially in fields such as gaming, architectural visualization, and remote collaboration. A study of the region highlights North America and the Asia-Pacific as key players in this field of research. The research also discusses the difficulties involved in integrating SLAM into XR, such as handling the computational requirements, guaranteeing real-time processing, and accomplishing successful sensor fusion. This paper strives to provide a thorough overview of the current status of SLAM in XR, pinpointing gaps that already exist and suggesting directions for further research. These observations aim to help researchers, developers, and industry professionals who want to use SLAM's capabilities in the quickly changing XR field.

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