论文标题
具有新颖的连续地面估计方法的3D环境中动态对象识别的概率框架
A Probabilistic Framework for Dynamic Object Recognition in 3D Environment With A Novel Continuous Ground Estimation Method
论文作者
论文摘要
在本文中，在3D环境中开发并提出了一个概率框架，以用于动态对象识别。使用ROS中的C ++和Python开发软件包，该软件包执行检测和跟踪任务。此外，开发了一种基于新型的高斯工艺回归（GPR）方法，以检测常规，倾斜和粗糙的不同城市场景中的地面点。假定地面行为仅证明局部输入依赖性平滑度。获得内核的长度尺度。贝叶斯推理实现sing \ textit {maumment a postteriori}标准。假定对数 - 界限可能性函数是一个多任务目标函数，以表示每个帧在每个帧的地面无偏见的视图，因为相邻的段可能在不均匀的场景中没有相似的地面结构，而共享的超参数值也可能没有。仿真结果显示了所提出的方法在不均匀和粗糙场景中的有效性，这些方法的表现优于基于高斯工艺的相似地面分割方法。
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.
