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Body sensor calibration and construction of 3D maps for robot navigation using the framework of conformal geometric algebra

机译:使用共形几何代数框架对人体传感器进行校准并为机器人导航构建3D地图

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The research, described in this paper, concerns the robot indoor navigation, emphasizing the aspects of sensor model and calibration, environment representation, and self-localization. The main point is that combining all of these aspects, an effective navigation system is obtained. We present a model of the catadioptric image formation process. Our model simplifies the operations needed in the catadioptric image process. Once we know the model of the catadioptric sensor, we have to calibrate it with respect to the other sensors of the robot, in order to be able to fuse their information. When the sensors are mounted on a robot arm, we can use the hand-eye calibration algorithm to calibrate them. In our case the sensors are mounted on a mobile robot that moves over a flat floor, thus the sensors have less degrees of freedom. For this reason we develop a calibration algorithm for sensors mounted on a mobile robot. Finally, combining all the previous results and a scan matching algorithm that we develop, we build 3D maps of the environment. These maps are used for the self-localization of the robot and to carry out path following tasks. In this work we present experiments which show the effectiveness of the proposed algorithms.
机译:本文描述的这项研究涉及机器人室内导航,着重于传感器模型和校准,环境表示以及自定位等方面。要点是结合所有这些方面,可以获得有效的导航系统。我们提出了折反射成像过程的模型。我们的模型简化了折反射成像过程中所需的操作。一旦知道了折反射传感器的模型,就必须相对于机器人的其他传感器进行校准,以便能够融合其信息​​。当传感器安装在机械臂上时,我们可以使用手眼校准算法对其进行校准。在我们的情况下,传感器安装在可在平坦地板上移动的移动机器人上,因此传感器的自由度较小。因此,我们为安装在移动机器人上的传感器开发了一种校准算法。最后,结合所有先前的结果和我们开发的扫描匹配算法,我们构建了环境的3D地图。这些地图用于机器人的自动定位和执行路径跟踪任务。在这项工作中,我们目前的实验表明了所提出算法的有效性。

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