论文标题
自主导航中的对象识别和跟踪的新型感知算法框架
Novel Perception Algorithmic Framework For Object Identification and Tracking In Autonomous Navigation
论文作者
论文摘要
本文介绍了一个新颖的感知框架，该框架能够在自动驾驶汽车的视野中识别和跟踪对象。拟议的算法不需要任何培训来实现这一目标。该框架利用自我车辆的姿势估计和基于KD-Tree的分割算法来生成对象簇。反过来，使用VFH技术，将每个识别对象群集的几何形状转换为多模式PDF，并使用每个新对象群集启动运动模型，以实现强大的时空跟踪。该方法进一步使用了高维概率密度函数和贝叶斯运动模型估算的统计特性，以从框架到框架识别和跟踪对象。该方法的有效性在KITTI数据集上进行了测试。结果表明，中值跟踪精度约为91％，端到端计算时间为153毫秒
This paper introduces a novel perception framework that has the ability to identify and track objects in autonomous vehicle's field of view. The proposed algorithms don't require any training for achieving this goal. The framework makes use of ego-vehicle's pose estimation and a KD-Tree-based segmentation algorithm to generate object clusters. In turn, using a VFH technique, the geometry of each identified object cluster is translated into a multi-modal PDF and a motion model is initiated with every new object cluster for the purpose of robust spatio-temporal tracking. The methodology further uses statistical properties of high-dimensional probability density functions and Bayesian motion model estimates to identify and track objects from frame to frame. The effectiveness of the methodology is tested on a KITTI dataset. The results show that the median tracking accuracy is around 91% with an end-to-end computational time of 153 milliseconds
