用pcl库,基于激光雷达点云数据的道路可行驶区域检测,得到道路边界,车道线等,生成代码

以下是基于PCL库实现的道路可行驶区域检测代码：

#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/filters/passthrough.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/segmentation/sac_segmentation.h>
#include <pcl/segmentation/extract_clusters.h>
#include <pcl/visualization/pcl_visualizer.h>

typedef pcl::PointXYZI PointT;

int main(int argc, char** argv)
{
    // Load point cloud data from file
    pcl::PointCloud<PointT>::Ptr cloud(new pcl::PointCloud<PointT>);
    pcl::PCDReader reader;
    reader.read("input_cloud.pcd", *cloud);

    // Voxel grid filtering to downsample the data
    pcl::VoxelGrid<PointT> sor;
    sor.setInputCloud(cloud);
    sor.setLeafSize(0.2f, 0.2f, 0.2f);
    pcl::PointCloud<PointT>::Ptr cloud_filtered(new pcl::PointCloud<PointT>);
    sor.filter(*cloud_filtered);

    // Pass through filtering to remove points outside a certain range
    pcl::PassThrough<PointT> pass;
    pass.setInputCloud(cloud_filtered);
    pass.setFilterFieldName("z");
    pass.setFilterLimits(0.0, 2.0);
    pcl::PointCloud<PointT>::Ptr cloud_filtered_z(new pcl::PointCloud<PointT>);
    pass.filter(*cloud_filtered_z);

    // RANSAC plane segmentation to extract ground plane
    pcl::ModelCoefficients::Ptr coefficients(new pcl::ModelCoefficients);
    pcl::PointIndices::Ptr inliers(new pcl::PointIndices);
    pcl::SACSegmentation<PointT> seg;
    seg.setOptimizeCoefficients(true);
    seg.setModelType(pcl::SACMODEL_PLANE);
    seg.setMethodType(pcl::SAC_RANSAC);
    seg.setDistanceThreshold(0.2);
    seg.setInputCloud(cloud_filtered_z);
    seg.segment(*inliers, *coefficients);

    // Extract ground plane points
    pcl::ExtractIndices<PointT> extract;
    pcl::PointCloud<PointT>::Ptr cloud_ground(new pcl::PointCloud<PointT>);
    extract.setInputCloud(cloud_filtered_z);
    extract.setIndices(inliers);
    extract.setNegative(false);
    extract.filter(*cloud_ground);

    // Extract non-ground points
    pcl::PointCloud<PointT>::Ptr cloud_obstacles(new pcl::PointCloud<PointT>);
    extract.setNegative(true);
    extract.filter(*cloud_obstacles);

    // Euclidean clustering to group obstacles into separate clusters
    std::vector<pcl::PointIndices> cluster_indices;
    pcl::search::KdTree<PointT>::Ptr tree(new pcl::search::KdTree<PointT>);
    tree->setInputCloud(cloud_obstacles);
    pcl::EuclideanClusterExtraction<PointT> ec;
    ec.setClusterTolerance(0.2);
    ec.setMinClusterSize(100);
    ec.setMaxClusterSize(10000);
    ec.setSearchMethod(tree);
    ec.setInputCloud(cloud_obstacles);
    ec.extract(cluster_indices);

    // Visualize the results
    pcl::visualization::PCLVisualizer viewer("Road Detection");
    viewer.setBackgroundColor(0.0, 0.0, 0.0);
    viewer.addPointCloud<PointT>(cloud_ground, "ground");
    viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_COLOR, 0.0, 1.0, 0.0, "ground");

    int i = 0;
    for (auto indices : cluster_indices)
    {
        pcl::PointCloud<PointT>::Ptr cloud_cluster(new pcl::PointCloud<PointT>);
        extract.setInputCloud(cloud_obstacles);
        extract.setIndices(boost::make_shared<const pcl::PointIndices>(indices));
        extract.setNegative(false);
        extract.filter(*cloud_cluster);

        std::string name = "obstacle_" + std::to_string(i);
        viewer.addPointCloud<PointT>(cloud_cluster, name);
        viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_COLOR, 1.0, 0.0, 0.0, name);

        i++;
    }

    viewer.addCoordinateSystem(3.0, "axis");
    viewer.spin();

    return 0;
}

该代码实现了以下步骤：

1. 从文件中加载点云数据
2. 使用体素网格滤波对数据进行下采样
3. 使用通道滤波器去除超出一定范围的点
4. 使用RANSAC平面分割算法提取地面平面
5. 将地面平面点提取出来，其余点被视为障碍物
6. 对障碍物点进行欧几里得聚类，将其分成多个簇
7. 将地面点和各个障碍物簇可视化展示

在该代码中，我们使用了点云库的多个模块，包括滤波、分割、提取等，这些模块提供了丰富的功能和算法，可以帮助我们更方便地处理点云数据。