“Focusing on the right regions” — Guided saliency prediction for visual SLAM

Jin, S.; Dai, Xu-Yang; Meng, Qinghao

doi:10.1016/j.eswa.2022.119068

Cited by 8 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…To some extent, efficiency is sacrificed to enhance the robustness and accuracy of VO/VSLAM systems. To this end, it is vital to strike a balance between accuracy and efficiency while deploying deep learning-based features into VO/VSLAM systems [23]- [25], especially for UAV platforms with limited payload capacity. To the best of our knowledge, the GCNv2tiny based SLAM [26] is the only learned feature-based VSLAM system that achieves real-time performance on the most popular UAV onboard computing platform, the Nvidia Jetson TX2.…”

Section: Introductionmentioning

confidence: 99%

A Robust Learned Feature-Based Visual Odometry System for UAV Pose Estimation in Challenging Indoor Environments

Yang

et al. 2023

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

Unmanned Aerial Vehicles (UAVs) are becoming popular nowadays due to their versatility and flexibility for indoor applications, such as the autonomous visual inspection for the inner surface of a pressure vessel. Nevertheless, robust and reliable position estimation is critical for completing these tasks. Visual Odometry (VO) and Visual Simultaneous Localisation and Mapping (VSLAM) allow the UAV to estimate its position in unknown environments. However, traditional feature-based VO/VSLAM systems struggle to deal with complex scenes such as low illumination and textureless environments. Replacing the traditional features with deep learning-based features provides the advantage of handling the challenging environment, but the efficiency is ignored. In this work, an efficient VO system based on a novel lightweight feature extraction network for UAV onboard platforms has been developed. The Deformable Convolution (DFConv) is utilised to improve the feature extraction capability. Owing to the limited onboard computing capability, the Depthwise Separable Convolution (DWConv) is adopted to calculate the offsets for the deformable convolution and construct the backbone network to improve the feature extraction efficiency. Experiments on public datasets indicate that the efficiency of the VO system is improved by 30.03% while preserving the accuracy on embedded platforms with the feature points and descriptors detected by the proposed Convolutional Neural Network (CNN). Moreover, the proposed VO system is verified through UAV flying tests in a real-world scenario. The results prove that the proposed VO system is able to handle the challenging environments where both the latest traditional and deep learning feature-based VO/VSLAM systems fail, and it is feasible for UAV self-localisation and autonomous navigation in the confined, low illumination and textureless indoor environment.

show abstract