基于平面拟合的地面点云精确分割方法

Accurate segmentation method of ground point cloud based on plane fitting

针对点云数据中的地面点会影响环境感知的精度与速度的问题,提出了一种基于平面拟合的地面点云精确分割方法。首先,根据投影距离将场景点云分为多个区域;其次,根据区域内的平均高度、分割地面点和地平面法向量的方向,确定地面平面拟合点并对地面平面进行拟合;最后,根据点到地面平面的距离,实现地面分割。本文基于KITTI数据集和实采数据将本文算法与RANSAC、GPF、R-GPF和PatchWork四种算法进行了对比,验证了区域划分、拟合点筛选以及地面平面法向量方向筛选对地面分割的有效性。实验结果表明,进行区域划分后,可以分割远距离稀疏地面;进行拟合点筛选后,在低迭代的条件下地面分割准确率达到0.9417;进行地平面法向量方向筛选后,能够避免将墙面拟合成地面;本文方法在F1分数、召回率和准确率上优于所对比的4种算法,速度可以达到42.78 Hz,能够精确、快速地对地面进行分割。

Aiming at the problem that ground points in point cloud data can affect the precision and speed of environment perception,an accurate segmentation method of ground point cloud based on plane fitting was proposed.Firstly,the scene point cloud was divided into several areas based on the projection distance.Secondly,according to the average height in the area,the divided ground points and the normal vector direction of the ground plane,the ground plane fitting point was determined,and the ground plane was fitted.Finally,according the distance from the point to the ground plane to achieve ground segmentation.Using the KITTI dataset and the collected point cloud data to compare the proposed algorithm with four algorithms:RANSAC、GPF、R-GPF and PatchWork,verify the effectiveness of area division,fitting point screening and ground plane normal vector direction screening for ground segmentation.The experimental results show that after the area division,the far-distance sparse ground can be divided;after the fitting point screening,the ground segmentation accuracy reaches 0.9417 under the condition of low iteration.After screening the normal vector direction of the ground plane,fitting the wall to the ground was avoided.The proposed method is better than the four compared algorithms in terms of F1 score,recall rate and accuracy rate,and the speed can reach 42.78 Hz,which can divide the ground accurately and quickly.