- 简介
在激光雷达的特征提取中，对整帧的点云数据进行分割是至关重要的，但是非常明显的是在3D场景中，捕获的点云通常是稀疏且非结构化的，分割有可能误分割或者漏分割。今天我们来看一下22年的论文《FEC: Fast Euclidean Clustering for Point Cloud Segmentation》，这篇文章中提出一种新颖的快速欧几里得聚类算法，同时据作者说易于实现，具体只有40行的CPP代码。目前作者代码还没有开源，说是accept后就开源。虽然这篇文章并没有被大量的关注，但是从小代码，即插即用等特点，这给我们可以通过文章学习它的详细思想以及算法的可能性。
- 文章贡献
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* 基于聚类的方法。聚类算法根据元素的相似性将元素划分为类别，可应用于点云分割。因此，K均值、均值漂移、DBSCAN和欧几里德聚类提取（EC）常被用于这项任务，尽管基于聚类的方法简单，但点云中每个点的高迭代率导致了高计算负担并降低了效率。
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; 2. 算法推导
2.1地面滤除
首先文中提到会先对地面点进行滤除，虽然说是地面滤除，但是如果说我们需要实际上提取车道线点时候，可以直接使用地面提取，然后再对地面的特征点进行提取。文中提到使用了cloth simulation filter来提取和滤除接地点云。
CSF csf;
csf.readPointsFromFile(terr_pointClouds_filepath);
clock_t start, end;
start = clock();
csf.params.bSloopSmooth = ss;
csf.params.class_threshold = atof(class_threshold.c_str());
csf.params.cloth_resolution = atof(cloth_resolution.c_str());
csf.params.interations = atoi(iterations.c_str());
csf.params.rigidness = atoi(rigidness.c_str());
csf.params.time_step = atof(time_step.c_str());
std::vector<int> groundIndexes, offGroundIndexes;
if (argc == 2 && strcmp(argv[1], "-c")==0)
{
std::cout << "Export cloth enabled." << std::endl;
csf.do_filtering(groundIndexes, offGroundIndexes, true);
}
else
{
csf.do_filtering(groundIndexes, offGroundIndexes, false);
}
end = clock();
double dur = (double)(end - start);
printf("Use Time:%f\n", (dur / CLOCKS_PER_SEC));
csf.savePoints(groundIndexes,"ground.txt");
csf.savePoints(offGroundIndexes, "non-ground.txt");
2.2 快速欧几里得聚类
与EC类似，我们使用欧几里得（L2）距离度量来测量无组织点云的接近度，并将相似性分组到同一聚类中，可以描述为
m i n ∥ P i − P i ′ ∥ 2 ⩾ d t h min\|\mathbf{P}i – \mathbf{P}{i’}\|2 \geqslant d{th}m i n ∥P i −P i ′∥2 ⩾d t h
伪代码步骤为：
流程整体来说还是比较清楚的，首先会将P i \mathbf{P}i P i 的label全部置零，然后分割的s e g L a b segLab s e g L a b初始化为1，然后遍历所有的点，当发现遍历的点中label为0，则去找最近的关联点P N N \mathbf{P}{NN}P N N 。如果存在有点不为0，则找到最小的label值，这就代表存在联通。如果不存在则赋值，然后对P N N \mathbf{P}_{NN}P N N 遍历，如果找到其他的label大于m i n S e g L a b minSegLab m i n S e g L a b的值，则再次遍历所有的带你，然后将所有其他的label值，重置为m i n S e g L a b minSegLab m i n S e g L a b的值。
文中提到本文的算法使用逐点方案，该逐点方案的输入编号顺序与EC和EG中使用的聚类方案相反，FEC易于部署，在C++中仅需40行代码。
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; …详情请参照 古月居
Original: https://blog.csdn.net/lovely_yoshino/article/details/126930260
Author: Hermit_Rabbit
Title: 经典文献阅读之–FEC
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