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首页> 外文会议>International Conference on Inventive Systems and Control >Point cloud clustering algorithm for autonomous vehicle based on 2.5D Gaussian Map
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Point cloud clustering algorithm for autonomous vehicle based on 2.5D Gaussian Map

机译：基于2.5D高斯地图的自主车辆点云聚类算法

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摘要

Multi-beam Lidars have many good features, such as: high accuracy, wide range, strong anti-interference capacity, and the ability of obtain multiple information (depth, reflectivity) and so on, which excellent performance gain greatly attention by the unmanned system industry, and it is very applicable to the environment perception of autonomous cars. But the vast amount of data and the sparsity make its point cloud very difficult to process. In this paper, a 2.5D map based on Gaussian distribution is purposefully proposed, and a composite distance is used to cluster the point cloud. Experiments show that this clustering method can effectively solve the problem that the same obstacle cannot gather together caused by shadow or the sparsity of point cloud. Compared to previous methods, it achieves excellent performance with high accuracy and stable robustness and meet the requirements of real-time system with less than 100ms average running time.
机译：多光束楣有许多良好的功能，如：高精度，范围广，抗干扰能力强，以及获得多种信息（深度，反射率）等的能力，这是一种绝佳的系统对无人机的关注行业，它非常适用于自动驾驶汽车的环境感知。但大量的数据和稀疏使其点云很难处理。本文采用了基于高斯分布的2.5D地图，并采用了复合距离来聚类点云。实验表明，这种聚类方法可以有效地解决了相同障碍物不能聚集在一起的问题或点云的稀缺来聚集在一起的问题。与以前的方法相比，它具有优异的性能，具有高精度和稳定的稳健性，并满足实时系统的要求，平均运行时间不到100ms。

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