Search

Your search keyword '"Kong, Dezhi"' showing total 626 results
Start Over Author "Kong, Dezhi" Publication Year Range Last 50 years
626 results on '"Kong, Dezhi"'

1. 3D Gaussian Splatting in Robotics: A Survey

Author

Zhu, Siting, Wang, Guangming, Kong, Xin, Kong, Dezhi, and Wang, Hesheng

Subjects
Computer Science - Robotics
Abstract

Dense 3D representations of the environment have been a long-term goal in the robotics field. While previous Neural Radiance Fields (NeRF) representation have been prevalent for its implicit, coordinate-based model, the recent emergence of 3D Gaussian Splatting (3DGS) has demonstrated remarkable potential in its explicit radiance field representation. By leveraging 3D Gaussian primitives for explicit scene representation and enabling differentiable rendering, 3DGS has shown significant advantages over other radiance fields in real-time rendering and photo-realistic performance, which is beneficial for robotic applications. In this survey, we provide a comprehensive understanding of 3DGS in the field of robotics. We divide our discussion of the related works into two main categories: the application of 3DGS and the advancements in 3DGS techniques. In the application section, we explore how 3DGS has been utilized in various robotics tasks from scene understanding and interaction perspectives. The advance of 3DGS section focuses on the improvements of 3DGS own properties in its adaptability and efficiency, aiming to enhance its performance in robotics. We then summarize the most commonly used datasets and evaluation metrics in robotics. Finally, we identify the challenges and limitations of current 3DGS methods and discuss the future development of 3DGS in robotics.

Published
2024

19. Settings Optimization Method for Zero-Sequence Directional Overcurrent Relays Based on Coordination Time Interval

Author

Gao, Yukun, Gao, Yi, Chen, Jinfu, Shi, Hengchu, Kong, Dezhi, Chen, Jing, You, Hao, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Zeng, Pingliang, editor, Zhang, Xiao-Ping, editor, Terzija, Vladimir, editor, Ding, Yi, editor, and Luo, Yunxia, editor

Published
2023
Full Text
View/download PDF

38. Study on upper limb joint angle prediction method based on sEMG

Author

KONG Dezhi, WANG Wendong, GUO Dong, and SHI Yikai

Subjects
上肢外骨骼, 表面肌电信号, 最小二乘支持向量机, 连续运动估计, Motor vehicles. Aeronautics. Astronautics, TL1-4050
Abstract

Aiming at the problems of insufficient human-computer interaction and human-machine coupling in the rehabilitation training process, a prediction model of upper limb joint angle is proposed and verified by experiments. Firstly, a mixture vector that can well represent the motion intention of the upper limbs is obtained based on sEMG; secondly, the signal preprocessing, feature optimization and extraction of temporal eigenvalues are completed; finally, for the problems of unsatisfactory prediction accuracy and slow prediction speed of the current models in the field of motion control, the least square method (LSM) is adopted. The upper limb joint angle prediction is realized by multiplying the support vector machine (LSSVM) first. The experimental results show that the prediction model proposed in this paper can well predict the motion trajectory of the upper limb joints of the human body according to the sEMG and attitude information, effectively reduce the prediction time delay and error, and has certain advantages.

Published
2022
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results