easy_handeye:自动,独立于硬件的手眼校准 该软件包提供功能和GUI来: 通过tf采样机器人位置和跟踪系统的输出, 通过OpenCV库的Tsai-Lenz算法实现,计算基于眼图或手眼的校准矩阵, 存储校准结果, 在每次后续系统启动时,将校准过程的结果作为tf转换发布, (可选)通过MoveIt!自动围绕起始姿势移动机器人MoveIt! 获取样品。 预期的结果是使校准变得容易和直接,并使整个系统保持最新。 提供了两个要运行的启动文件,分别用于执行校准和检查其结果。 可以将另一个启动文件集成到您自己的启动文件中,以透明的方式使用校准结果:如果再次执行校准,则将使用更新的结果,而无需采取进一步的措施。 您可以通过在模拟器中试用该软件。 该软件包还作为将easy_handeye集成到您自己的启动脚本中的示例。 消息 版本0.4.2 修复了手绘机器人运动场景 版本0.4.1
2021-12-08 15:36:10 6.26MB robot camera ros calibration
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Factor Graphs for Robot Perception.pdf Factor Graphs for Robot Perception.pdf Factor Graphs for Robot Perception.pdf Factor Graphs for Robot Perception.pdf Factor Graphs for Robot Perception.pdf Factor Graphs for Robot Perception.pdf
2021-12-08 08:04:30 4.35MB SLAM
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ABB Robot PCSDK 6.05.7363 ABB Robot PCSDK 6.05.7363 ABB Robot PCSDK 6.05.7363
2021-12-02 13:22:41 62.04MB ABB Robot PCSDK 6.05.7363
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| | ROS Web组件 一个JavaScript库,用于快速开发连接的Web界面。 通过。 在( ) 。 该库提供了,这些与JavaScript函数接口以抽象化 ,简化了发布和订阅主题,从而为一组常见的机器人行为和数据源进行单行函数调用或仅编写HTML标签。 这些功能分为两类: ,触发机器人行为。 ,从机器人返回数据。 建立 将此存储库中的文件复制到网站的根目录中,并在要使用“ roswebcomponents”的任何页面的<head>标记中粘贴以下内容,以包括该库及其JS和CSS依赖项: < link rel =" stylesheet " href =" styles/jqu
2021-12-01 14:40:22 200KB ui robot web-app web-components
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We review the use of factor graphs for the modeling and solving of large-scale inference problems in robotics. Factor graphs are a family of probabilistic graphical models, other examples of which are Bayesian networks and Markov random fields, well known from the statistical modeling and machine learning literature. They provide a powerful abstraction that gives insight into particular inference problems, making it easier to think about and design solutions, and write modular software to perform the actual inference. We illustrate their use in the simultaneous localization and mapping problem and other important problems associated with deploying robots in the real world. We introduce factor graphs as an economical representation within which to formulate the different inference problems, setting the stage for the subsequent sections on practical methods to solve them.We explain the nonlinear optimization techniques for solving arbitrary nonlinear factor graphs, which requires repeatedly solving large sparse linear systems. The sparse structure of the factor graph is the key to understanding this more general algorithm, and hence also understanding (and improving) sparse factorization methods. We provide insight into the graphs underlying robotics inference, and how their sparsity is affected by the implementation choices we make, crucial for achieving highly performant algorithms. As many inference problems in robotics are incremental, we also discuss the iSAM class of algorithms that can reuse previous computations, re-interpreting incremental matrix factorization methods as operations on graphical models, introducing the Bayes tree in the process. Because in most practical situations we will have to deal with 3D rotations and other nonlinear manifolds, we also introduce the more sophisticated machinery to perform optimization on nonlinear manifolds. Finally, we provide an overview of applications of factor graphs for robot perception, showing the broad impact fa
2021-11-30 16:18:40 1.38MB Factor Graphs Robot Perception
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State Estimation and Optimization for Mobile Robot Navigation
2021-11-30 15:27:53 16.5MB Mobile Robot Navigation
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出版社: Springer; 1999 (2012年11月14日) 平装: 205页 语种: 英语 ISBN: 1461369827 条形码: 9781461369820 商品尺寸: 15.5 x 1.3 x 23.5 cm 商品重量: 318 g ASIN: 1461369827
2021-11-30 15:19:32 12.62MB SLAM
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本学期学习了计算机图形学,课程设计是要用OpenGL实现机器人。本项目代码简洁,注释比较多,能够轻松看懂代码,实现了某场景中机器人的移动,机器人身体部位的变换等,仅供参考,希望能够对大家有所帮助。
2021-11-30 14:00:34 29.78MB OpenGL robot 旋转
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This book is the result of inspirations and contributions from many researchers and students at the Swiss Federal Institute of Technology Lausanne (EPFL), Carnegie Mellon University’s Robotics Institute, Pittsburgh (CMU), and many others around the globe.
2021-11-28 16:56:41 8.22MB ROBOT
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