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A Hierarchical LiDAR Odometry via Maximum Likelihood Estimation With Tightly Associated Distributions 期刊论文
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2022, 卷号: 71, 期号: 10, 页码: 10254-10268
作者:  Wang, Chengpeng;  Cao, Zhiqiang;  Li, Jianjie;  Liang, Shuang;  Tan, Min;  Yu, Junzhi
收藏  |  浏览/下载：164/0  |  提交时间：2022/12/27
Point cloud compression  Laser radar  Feature extraction  Smoothing methods  Three-dimensional displays  Optimization  Simultaneous localization and mapping  3D LiDAR odometry  fixed-lag smoothing  hierarchical optimization  maximum likelihood estimation  
Category-Level 6D Object Pose Estimation With Structure Encoder and Reasoning Attention 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2022, 卷号: 32, 期号: 10, 页码: 6728-6740
作者:  Liu, Jierui;  Cao, Zhiqiang;  Tang, Yingbo;  Liu, Xilong;  Tan, Min
收藏  |  浏览/下载：237/0  |  提交时间：2022/11/14
Shape  Three-dimensional displays  Cognition  Pose estimation  Feature extraction  Decoding  Solid modeling  Category-level  6D object pose estimation  structure encoder  reasoning attention  
Robot Navigation Based on Situational Awareness 期刊论文
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, 2022, 卷号: 14, 期号: 3, 页码: 869-881
作者:  Liu, Xilong;  Cao, Zhiqiang;  Yu, Yingying;  Ren, Guangli;  Yu, Junzhi;  Tan, Min
收藏  |  浏览/下载：234/0  |  提交时间：2022/11/14
Navigation  Robots  Robot sensing systems  Fitting  Sensors  Computational modeling  Three-dimensional displays  Path perception  robot navigation  situational awareness fitting network  
Finite-Time Stability Control of Uncertain Nonlinear Systems With Self-Limiting Control Terms 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 页码: 6
作者:  Zhu, Jiaming;  Yang, Yuequan;  Zhang, Tianping;  Cao, Zhiqiang
收藏  |  浏览/下载：223/0  |  提交时间：2022/06/06
Artificial neural networks  Nonlinear systems  Control systems  Adaptive systems  Backstepping  Biological neural networks  Trajectory  Backstepping  dynamic surface control (DSC)  finite-time tracking  neural network (NN)  self-limiting term