在移动机器人领域，从点云数据序列中跟踪空间的物体是一个非常重要的研究领域，其用于机器人对环境的动态感知从而做出正确的决策，一种典型的应用就是视觉伺服系统，其关键技术就是估计对象的位姿。来自东京大学的JSK lab的Ryohei Ueda在Willow Garage实习期间，开发PCL中的3D跟踪模块，该模块主要是提供利用空间几何、颜色等数据通用的位姿估计算法。
敬请关注PCL（Point Cloud Learning）中国关于libpcl_tracking的教程。
Tracking 3D objects with point cloud library
Tracking 3D objects in continuous point cloud data sequences is an important research topic for mobile robots: it allows robots to monitor the environment and make decisions and adapt their motions according to the changes in the world. An example of such a typical application is visual servoing, with its key challenge to estimate the three dimensional pose of an object in real-time.
During his internship at Willow Garage, Ryohei Ueda from the JSK laboratory at University of Tokyo, worked on a novel 3D tracking library for the Point Cloud Library (PCL) project. The purpose of the library is to provide a comprehensive algorithmic base for the estimation of 3D object poses using Monte Carlo sampling techniques and for calculating the likelihood using combined weighted metrics for hyper-dimensional spaces including Cartesian data, colors, and surface normals. The libpcl_tracking library is optimized to perform computations in real-time, by employing multi CPU cores optimization, adaptive particle filtering (KLD sampling) and other modern techniques.