基于改进欧氏聚类的锥桶检测方法与试验

Detection Method and Experiment of the Cone Based on Improved Euclidean Clustering

针对赛道环境下欧氏聚类算法检测锥桶不准确的问题,提出了一种基于改进欧氏聚类算法的锥桶检测方法。首先,通过机器人操作系统(ROS)采集点云;再对点云预处理,找到感兴趣区域(ROI)后,利用随机采样算法分离地面和锥桶的点云;然后,将距离和阈值模型化;接着,设计出面向赛道环境的区域划分方法来改进欧氏聚类算法,利用动态阈值聚类分割出锥桶点云;最后,通过Matlab平台验证算法。在两种赛道环境下进行实车试验,聚类分割的准确率分别达到93.98%和99%。试验结果表明,所提方法能够准确地检测赛道中的锥桶。

Aiming at the inaccurate detection of cone buckets by Euclidean clustering algorithm in the race track environment, a cone bucket detection method based on improved Euclidean clustering algorithm is proposed. Firstly, the point clouds are collected through the robot operating system(ROS);then the point clouds are preprocessed to find the region of interest(ROI) and then the ground and cone bucket point clouds are separated using a random sampling algorithm;then the distances and thresholds are modeled;then a region partitioning method is designed for the track environment to improve Euclidean clustering algorithm, and the cone bucket point clouds are segmented using dynamic threshold clustering;finally the algorithm is validated through the Matlab platform. The accuracy of clustering segmentation reached 93.98% and 99% in real-world tests under two track environments, respectively. The test results show that the proposed method can accurately detect the cone bucket in the track.