基于自适应阈值的循环增长地面点云分割算法

Ground Point Cloud Segmentation Algorithm with Cyclic Growth Based on Adaptive Threshold

在坡度、路面起伏不平的场景中,针对单一阈值敏感性不足,多阈值简单组合存在冗余计算,且影响分割精度和鲁棒性的问题,提出了一种基于阈值自适应选取的点云分割方法。该方法在栅格分割中引入循环增长判断,以兼顾局部特征与全局特征,设计了一种触发式双特征判断机制,并通过拟合地面波动幅度自适应确定分割的高度阈值提高算法的分割效率和分割精度。最后基于开源数据集进行了仿真测试,验证了所提算法在地面点云分割中的精确性和时效性。

In the scene of uneven slope and road surface,a point cloud segmentation method based on adaptive selection of threshold values was proposed to solve the problems of insufficient sensitivity of a single threshold value,redundancy calculation of simple combination of multiple thresholds,and impact on segmentation accuracy and robustness.Cyclic growth judgment was introduced into grid segmentation to give consideration to both local features and global features.A trigger dual-feature judgment mechanism was designed,and the segmentation efficiency and accuracy of the algorithm were improved by adaptive determination of the height threshold of segmentation by fitting the amplitude of ground fluctuation.Finally,simulation tests were carried out based on open source data set to verify the accuracy and timeliness of the proposed algorithm in ground point cloud segmentation.