In this thesis a probabilistic framework is developed and proposed for Dynamic Object Recognition in 3D Environments. A software package is developed using C++ and Python in ROS that performs the detection and tracking task. Furthermore, a novel Gaussian Process Regression (GPR) based method is developed to detect ground points in different urban scenarios of regular, sloped and rough. The ground surface behavior is assumed to only demonstrate local input-dependent smoothness. kernel's length-scales are obtained. Bayesian inference is implemented sing \textit{Maximum a Posteriori} criterion. The log-marginal likelihood function is assumed to be a multi-task objective function, to represent a whole-frame unbiased view of the ground at each frame because adjacent segments may not have similar ground structure in an uneven scene while having shared hyper-parameter values. Simulation results shows the effectiveness of the proposed method in uneven and rough scenes which outperforms similar Gaussian process based ground segmentation methods.

翻译：在此论文中,为三维环境中的动态物体识别开发了概率框架,并提出了3D环境中的动态物体识别方法。开发了一个软件包,该软件包使用C++和ROS中的Python来进行检测和跟踪任务。此外,还开发了一个基于Gaussian进程回归(GPR)的新颖方法,在常规、坡度和粗糙的不同城市情景中探测地面点。地面行为假定仅显示本地投入依赖的平稳性。获得了内核的长度尺度。Bayesian推论应用了歌唱 \ textit{Meximum aposeriori} 标准。日志边际概率函数被假定为多任务目标功能,代表每个框架对地面的全框架的无偏观,因为相邻部分在超参数值共享的场景点上可能没有相似的地面结构。模拟结果显示拟议方法在不均匀且优于类似高斯进程地面分割方法的分布的有效性。