A dynamic detection and data association method based on probabilistic models for visual SLAM

Zhang, Jianbo; Yuan, Liang; Ran, Teng; Peng, Song; Tao, Qing; Xiao, Wendong; Cui, Jianping

doi:10.1016/j.displa.2024.102663

Cited by 4 publications

(1 citation statement)

References 42 publications

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“…Moreover, the incorporation of control and estimation methods into many area [15][16][17][18][19][20][21][22][23][24][25][26][27][28], including into SLAM algorithms further enhances accuracy and reliability in dynamic environments, bridging the gap between theoretical modeling and real-world applications [29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44]. The progression of SLAM, starting with its basic algorithms and extending to its use in XR, signifies a crucial moment in the story of computer sciences and human-computer interaction [45][46][47][48][49][50][51][52][53]. SLAM technologies are now seen as crucial for creating fully immersive virtual, augmented, and mixed realities, defining the forefront of XR development.…”

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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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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DZ-SLAM: A SAM-based SLAM algorithm oriented to dynamic environments

Chen,

Zang,

Zhang

2024

Displays

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YOD-SLAM: An Indoor Dynamic VSLAM Algorithm Based on the YOLOv8 Model and Depth Information

Li,

Wang,

et al. 2024

Electronics

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Aiming at the problems of low positioning accuracy and poor mapping effect of the visual SLAM system caused by the poor quality of the dynamic object mask in an indoor dynamic environment, an indoor dynamic VSLAM algorithm based on the YOLOv8 model and depth information (YOD-SLAM) is proposed based on the ORB-SLAM3 system. Firstly, the YOLOv8 model obtains the original mask of a priori dynamic objects, and the depth information is used to modify the mask. Secondly, the mask’s depth information and center point are used to a priori determine if the dynamic object has missed detection and if the mask needs to be redrawn. Then, the mask edge distance and depth information are used to judge the movement state of non-prior dynamic objects. Finally, all dynamic object information is removed, and the remaining static objects are used for posing estimation and dense point cloud mapping. The accuracy of camera positioning and the construction effect of dense point cloud maps are verified using the TUM RGB-D dataset and real environment data. The results show that YOD-SLAM has a higher positioning accuracy and dense point cloud mapping effect in dynamic scenes than other advanced SLAM systems such as DS-SLAM and DynaSLAM.

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A dynamic detection and data association method based on probabilistic models for visual SLAM

Cited by 4 publications

References 42 publications

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

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

DZ-SLAM: A SAM-based SLAM algorithm oriented to dynamic environments

YOD-SLAM: An Indoor Dynamic VSLAM Algorithm Based on the YOLOv8 Model and Depth Information

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