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An Improved FastSLAM Algorithm Based on Revised Genetic Resampling and SR-UPF 期刊论文
International Journal of Automation and Computing, 2018, 卷号: 15, 期号: 3, 页码: 325-334
作者:  Tai-Zhi Lv;  Chun-Xia Zhao;  Hao-Feng Zhang
浏览  |  Adobe PDF(938Kb)  |  收藏  |  浏览/下载：109/39  |  提交时间：2021/02/23
Simultaneous localization and mapping (SLAM)  genetic algorithm  square root unscented particle filter (SR-UPF)  fast Metropolis-Hastings (MH)  double roulette wheels.  
Iterated Conditional Modes to Solve Simultaneous Localization and Mapping in Markov Random Fields Context 期刊论文
International Journal of Automation and Computing, 2018, 卷号: 15, 期号: 3, 页码: 310-324
作者:  J. Gimenez;  A. Amicarelli;  J. M. Toibero;  F. di Sciascio;  R. Carelli
浏览  |  Adobe PDF(1975Kb)  |  收藏  |  浏览/下载：119/43  |  提交时间：2021/02/23
Simultaneous localization and mapping  Markov random fields  iterated conditional modes  modelling  on-line solver.  
A Survey of the Research Status of Pedestrian Dead Reckoning Systems Based on Inertial Sensors 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 1, 页码: 65-83
作者:  Yuan Wu;  Hai-Bing Zhu;  Qing-Xiu Du;  Shu-Ming Tang
浏览  |  Adobe PDF(1248Kb)  |  收藏  |  浏览/下载：260/80  |  提交时间：2021/02/22
Inertial measurement unit (IMU)  pedestrian dead-reckoning  indoor navigation  technical route  general framework.  
A Survey of the Research Status of Pedestrian Dead Reckoning Systems Based on Inertial Sensors 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 1, 页码: 65-83
作者:  Wu, Yuan;  Zhu, Haibing;  Du, Qingxiu;  Tang, Shuming
Adobe PDF(989Kb)  |  收藏  |  浏览/下载：456/212  |  提交时间：2019/05/04
Inertial Measurement Unit (Imu)  Pedestrian Dead-reckoning  Indoor Navigation  Technical Route  General Framework  
视觉监控中的目标跟踪研究 学位论文
, 中国科学院自动化研究所: 中国科学院研究生院, 2010
作者:  李敏
Adobe PDF(7566Kb)  |  收藏  |  浏览/下载：214/0  |  提交时间：2015/09/02
Omega形状  方向滤波的金字塔统计量  简化的生物启发特征  姿态估计器  增量自调节粒子滤波  底层启示模型  Mcmc粒子滤波  Omega-shape  Pyramidal Statistics Of Oriented Filtering  Simplified Biologically Inspired Features  Pose Estimator  Incremental Self-tuning Particle Filtering  Low-level Cues  Mcmc-particle Filtering