提出了一种采用单目视觉和多超声传感器来识别门牌号的室内移动机器人全局自定位方法和导航策略。
This paper proposes a global self-localization method and navigation strategies for indoor mobile robot, based on monocular vision and multi-sonar sensors to recognize the doorplate.
研究了应用线性网络对组合导航多传感器信息进行融合的方法，在此基础上提出了一种神经网络组合导航容错算法。
In this paper, a method of multi-sensor data fusion using neural network in integrated navigation system is given, and then a fault-tolerant algorithm is proposed.
而分层融合结构作为多传感器信息融合的另一种重要的模式，在组合导航系统中应用尚未见报道。
However, the multi level fusion architecture, another important multisensor information fusion mode, has not been applied to the integrated navigation system as far as know.
当前应用于机器人的多传感器数据融合研究大都集中在机器人自主导航和定位问题上，很少涉及利用机器人进行目标跟踪的研究。
The existing researches of multisensor data fusion applied in robots focus mostly navigation and orientation, and a few re-searches about the object tracking using robots are reported.
研究移动机器人视觉系统及其摄像机标定、多传感器信息融合、导航控制理论与方法具有非常重要的意义。
It is significant to research on mobile robot vision system and the related technology, such as camera calibration, multi-sensor fusion, robot navigation and control theory.
目的通过估计误差方差阵，对多传感器组合导航系统中不同的融合数据进行定位精度比较，为系统定位提供选择数据的依据。
Aim Using the variance matrix of estimated error, positional accuracy can be compared with different mixing together data, it has put forward a kind of basis for system location to select data.
着眼于机器人导航中多传感器数据融合需求，提出了一种基于模糊贴近度的数据融合新算法，研究了它的原理及其在机器人导航中的应用过程。
According to the requirements of multi-sensor data fusion in robot navigation, a novel, fuzzy similarity-based fusion algorithm is given and its theory and application in robot navigation is studied.
着眼于机器人导航中多传感器数据融合需求，提出了一种基于模糊贴近度的数据融合新算法，研究了它的原理及其在机器人导航中的应用过程。
According to the requirements of multi-sensor data fusion in robot navigation, a novel, fuzzy similarity-based fusion algorithm is given and its theory and application in robot navigation is studied.
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