主要内容包括移动机器人的位姿跟踪方法、马尔可夫定位方法、蒙特卡洛定位方法，实时避障方法等。
The research topics include approaches of robots pose tracking, Markov localization, Monte Carlo localization, real-time obstacle avoidance.
其主要内容包括移动机器人的位姿跟踪方法、马尔可夫定位方法、粒子滤波方法及各种粒子滤波改进方法。
The research topics include approaches of robot's pose tracking, Markov localization, Particle Filter and other improved PF method.
位姿估计是移动机器人研究中的一个关键问题，对于运动目标跟踪、机器人导航、地图生成等具有重要意义。
Pose estimation is a key issue in mobile robots and important to moving objects tracking, robot navigation, map building, etc.
利用单目摄像头提取和跟踪环境特征点集，进而根据观测模型利用扩展卡尔曼滤波算法估算出机器人的位姿。
It extracts and tracks feature point sets in the environment with single camera, and then calculates position and pose of the robot with measurement model and extended Kalman filtering.
通过位置跟踪器获取操作者手掌的位姿数据，采用数据分解和矩阵变换的方法将其映射为虚拟手掌在虚拟现实环境中的位姿，实现了对虚拟手的准确控制。
The gestures of real palm that obtained by position tracker are mapped to the gestures of virtual palm in the virtual reality environment by data mapping and transfer matrixes.
建立了一种基于视觉位姿的机器人视觉伺服系统，该系统通过检测目标的位姿状态，从而实时监控和跟踪运动目标，实现对运动目标的相对距离和瞬时速度的动态检测。
A new method of visual location of mobile robot is put forward, and it can measure the relative distance between the mobile robot and goal by objective image.
在分析经典轨迹跟踪控制律缺点的基础上，算法中引入了机器人位姿误差的纵坐标误差以加速机器人的轨迹逼近速度，并采用人工场和位姿误差协同作用来共同完成机器人的导向控制。
In the proposed control method, a state differential feedback procedure is used to stabilize the robot and an artificial field combined with the posture error is introduced to navigate the robot.
在分析经典轨迹跟踪控制律缺点的基础上，算法中引入了机器人位姿误差的纵坐标误差以加速机器人的轨迹逼近速度，并采用人工场和位姿误差协同作用来共同完成机器人的导向控制。
In the proposed control method, a state differential feedback procedure is used to stabilize the robot and an artificial field combined with the posture error is introduced to navigate the robot.
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