里程计

Based on Monte Carlo localization, the system makes use of high-noised sensor data of artificial and natural landmarks, along with odometry data without feedback.

该系统基于Monte Carlo定位方法，分别利用了人工地标和自然地标的高噪声的传感器信息，结合无反馈的里程计信息来完成自身定位。

During robot motion, the information from feature observations is fused with that from the odometry by particle filter, which improves the speed and accuracy of the localization effectively.

在机器人移动过程中，环境特征点的观测信息和里程计信息通过粒子滤波相融合，从而提高了机器人定位的速度和精度。

A novel stereo visual odometry algorithm based on disparity space was proposed for real dynamic environments. Successive frames of stereo images were used.

针对实际的复杂动态场景，提出了一种基于视差空间的立体视觉里程计方法。

The navigation subsystem of the mobile robot fuses the position estimation obtained by a vision system with the position estimated by odometry using a Kalman filter.

移动机器人的导航子系统利用卡尔曼滤波器，融合由视觉系统与由里程计获得的位置估计值。

The robot estimates its pose recursively through a MAP estimator that incorporates the information collected from odometry and unidirectional camera.

机器人通过 MAP 估计器融合里程计和单向摄象机的图像信息递归估计其自身位姿状态。

As the bot blunders around, it relays back odometry readings, and the software estimates its position by dead reckoning.

机器人踉跄行进时，里程计会回报读数，而软体也会以「航位推算」估出它的位置。

Dead reckoning of mobile robot in complex terrain is analyzed by therigid-body kinematic constraints of mobile robot that is on the basis oflocomotion architecture with the wheeled and rocker-bogie suspension system.At the same time, the kinematic model of mobile robot is obtained using themultiple sensors information from odometry, fiber optic gyro, tilt sensor, et al.

根据刚体运动学的约束分析了一种轮式结构与悬浮式摇架系统相结合的移动机器人在复杂地形下的航迹推测，采用里程计、光纤陀螺仪、倾角传感器等传感器信息推导移动机器人的运动学模型，提出一种运动学模型与车轮。

The near-infrared illuminator and omni-directional vision are used for recognizing bar-coded landmarks. Data from vision system and odometry are fused with an extended Kalman filter to realize robot self-localization.

通过近红外光源照明，利用全景视觉识别采用条形编码格式的路标，并利用扩展卡尔曼滤波算法融合视觉数据和里程计数据，从而实现机器人自定位。

As one part of the project, the dissertation is developed with thelocalization problem in mobile robot navigation. Combined with "MobileRobot 1 of Central South university (MORCS-1)", a mobile robot designed byus that equipped with a 2D laser measurement system to sense the environmentand the proprioceptive sensors such as the odometry, gyroscope to calculate itsdead reckoning, the approach about the four kind uncertainty factors of mobilerobot localization is studied. These researches include that the error analysisand calibration of position sensors is implemented to reduce the measurementnoise, the 3D kinematic model of mobile robot is built to gain the accuratepose in complex terrain, some work on the automatic detection of static ordynamic obstacles based on laser scanner is investigated to eliminate thedynamic influence of the environment and to realize the reliably absoluteposition, and lastly a robust algorithm is presented to involve the incrementalenvironment mapping and self-localization of mobile robot with unknown dataassociation and to improve the self-localization performance of mobile robotunder unknown environment.

作为该项目研究的一部分，本论文以移动机器人导航中的定位问题为研究内容，利用自行研制的装配有二维激光雷达环境感知系统，并通过里程计、陀螺仪等内部传感器来实现航迹推测的移动机器人"中南移动1号MORCS-1"，重点围绕影响移动机器人系统定位的四类不确定性处理展开研究：通过移动机器人定位传感器的误差分析及校准，旨在消除传感器噪声所带来的测量误差；通过建立移动机器人的三维运动学模型进行航迹推测，以期实现复杂地形下精确的移动机器人本体姿态感受；通过基于激光雷达的动静态障碍的自主检测等相关研究，尽量消除环境的不确定因素影响进而实现可靠的移动机器人绝对定位；通过以上研究，针对未知数据关联下移动机器人增量式环境建图与自定位的研究提出一种鲁棒的滤波算法，改善未知环境中移动机器人自定位的性能。

The paper introduces the model and system error of SmartROB-2 mobile robots odometer and laser range finder firstly.

本文首先对SmartROB-2移动机器人所装配的里程计传感器和激光测距仪传感器的模型和误差分别进行了分析讨论。

This one mode pays close attention to network credence foundation of the businessman very much.

这一模式非常关注商人的网络信用基础。

Cell morphology of bacterial ghost of Pasteurella multocida was observed by scanning electron microscopy and inactivation ratio was estimated by CFU analysi.

扫描电镜观察多杀性巴氏杆菌细菌幽灵和菌落形成单位评价遗传灭活率。