Persistent Homology for Detection of Objects from Mobile LiDAR Point Cloud Data in Autonomous Vehicles
Persistent Homology for Detection of Objects from Mobile LiDAR Point Cloud Data in Autonomous Vehicles
复制标题
自动驾驶汽车中移动 LiDAR 点云数据中物体检测的持久同源性
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
H. Karimi
中科院分区：
文献类型：
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作者：
M. Syzdykbayev;H. Karimi
最近，研究人员高度关注与自动驾驶车辆的目标检测和计算机视觉相关的问题。此类车辆具有许多优点，包括能够帮助解决与交通相关的问题，例如安全问题、交通拥堵和整体流动性。多光束“光检测和测距”（LiDAR）是主要传感器之一，用于通过创建周围环境的点云数据图来感知和检测物体。当前仅使用移动激光雷达数据的物体检测任务将整个区域划分为立方体并采用图像物体检测方法。由于三维增加了计算时间，这种方法带来了挑战，因此需要在性能和时间优化之间进行权衡。在本文中，我们提出了一种通过研究对象的形状来使用点云数据检测对象的新方法。为此，我们开发了一种基于通过持久同源实现的拓扑数据分析的方法来分析数据的定性属性。据我们所知，我们的工作是第一个为现实世界移动激光雷达点云数据探索开发拓扑数据分析的工作。评估结果显示了使用从条形码提取的特征的高精度分类结果。
Recently, researchers have paid significant attention to problems related to object detection and computer vision for autonomous vehicles. Such vehicles offer many benefits, including their ability to help address transportation-related issues such as safety concerns, traffic jams, and overall mobility. Multi-beam ‘light detection and ranging’ (LiDAR) is one of the main sensors that is used to sense and detect objects by creating a point cloud data map of the surrounding environment. Current object detection tasks that use only mobile LiDAR data divide the entire area into cubes and employ image object detection methods. Such an approach poses challenges due to the third dimension that increases computational time, which thus requires a tradeoff between performance and time optimization. In this paper, we propose a new approach to detect objects using point cloud data by investigating the shapes of the objects. To this end, we developed a method based on topological data analysis achieved via persistent homology to analyze the qualitative properties of the data. To the best of our knowledge, our work is the first to develop topological data analysis for real-world mobile LiDAR point cloud data exploration. The evaluation result shows a high accuracy classification result using features extracted from barcodes.