用pcl库，进行道路可行驶区域检测,得到道路边界、车道线,代码

以下是基于PCL库进行道路可行驶区域检测的代码示例：

#include <iostream>
#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/filters/passthrough.h>
#include <pcl/segmentation/sac_segmentation.h>
#include <pcl/segmentation/extract_clusters.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/features/normal_3d.h>
#include <pcl/kdtree/kdtree.h>
#include <pcl/sample_consensus/method_types.h>
#include <pcl/sample_consensus/model_types.h>
#include <pcl/segmentation/sac_segmentation.h>
#include <pcl/segmentation/extract_clusters.h>
#include <pcl/visualization/pcl_visualizer.h>

typedef pcl::PointXYZRGB PointT;
typedef pcl::PointCloud<PointT> PointCloud;

int main(int argc, char **argv)
{
    // 读取点云数据
    PointCloud::Ptr cloud(new PointCloud);
    pcl::io::loadPCDFile<PointT>("input.pcd", *cloud);

    // 下采样
    pcl::VoxelGrid<PointT> voxel_filter;
    voxel_filter.setLeafSize(0.1, 0.1, 0.1);
    PointCloud::Ptr filtered_cloud(new PointCloud);
    voxel_filter.setInputCloud(cloud);
    voxel_filter.filter(*filtered_cloud);

    // 滤波
    pcl::PassThrough<PointT> pass;
    pass.setFilterFieldName("z");
    pass.setFilterLimits(0, 1.5);
    pass.setInputCloud(filtered_cloud);
    PointCloud::Ptr pass_filtered_cloud(new PointCloud);
    pass.filter(*pass_filtered_cloud);

    // 平面分割
    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.setMaxIterations(1000);
    seg.setDistanceThreshold(0.01);
    seg.setInputCloud(pass_filtered_cloud);
    seg.segment(*inliers, *coefficients);

    // 提取地面点
    pcl::ExtractIndices<PointT> extract;
    extract.setInputCloud(pass_filtered_cloud);
    extract.setIndices(inliers);
    extract.setNegative(false);
    PointCloud::Ptr ground_cloud(new PointCloud);
    extract.filter(*ground_cloud);

    // 提取非地面点
    extract.setNegative(true);
    PointCloud::Ptr non_ground_cloud(new PointCloud);
    extract.filter(*non_ground_cloud);

    // 计算法向量
    pcl::NormalEstimation<PointT, pcl::Normal> ne;
    pcl::PointCloud<pcl::Normal>::Ptr normals(new pcl::PointCloud<pcl::Normal>);
    pcl::search::KdTree<PointT>::Ptr tree(new pcl::search::KdTree<PointT>);
    tree->setInputCloud(non_ground_cloud);
    ne.setInputCloud(non_ground_cloud);
    ne.setSearchMethod(tree);
    ne.setKSearch(20);
    ne.compute(*normals);

    // 聚类
    pcl::search::KdTree<PointT>::Ptr cluster_tree(new pcl::search::KdTree<PointT>);
    cluster_tree->setInputCloud(non_ground_cloud);
    std::vector<pcl::PointIndices> cluster_indices;
    pcl::EuclideanClusterExtraction<PointT> ec;
    ec.setClusterTolerance(0.2);
    ec.setMinClusterSize(100);
    ec.setMaxClusterSize(10000);
    ec.setSearchMethod(cluster_tree);
    ec.setInputCloud(non_ground_cloud);
    ec.extract(cluster_indices);

    // 可视化
    pcl::visualization::PCLVisualizer viewer("PCL Viewer");
    viewer.setBackgroundColor(0, 0, 0);
    viewer.addPointCloud<PointT>(non_ground_cloud, "non_ground_cloud");
    viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "non_ground_cloud");

    // 绘制点云聚类结果
    int cluster_color[][3] = {{255, 0, 0}, {0, 255, 0}, {0, 0, 255}, {255, 255, 0}, {0, 255, 255}, {255, 0, 255}};
    int color_idx = 0;
    for (auto &indices : cluster_indices)
    {
        PointCloud::Ptr cluster_cloud(new PointCloud);
        for (auto &index : indices.indices)
        {
            cluster_cloud->points.push_back(non_ground_cloud->points[index]);
        }
        pcl::visualization::PointCloudColorHandlerCustom<PointT> single_color(cluster_cloud, cluster_color[color_idx % 6][0], cluster_color[color_idx % 6][1], cluster_color[color_idx % 6][2]);
        viewer.addPointCloud<PointT>(cluster_cloud, single_color, "cluster_" + std::to_string(color_idx));
        viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 3, "cluster_" + std::to_string(color_idx));
        color_idx++;
    }

    // 显示结果
    while (!viewer.wasStopped())
    {
        viewer.spinOnce();
    }

    return 0;
}

在上面的代码中，我们首先读取了点云数据，并进行了下采样和滤波。接下来，我们使用平面分割算法将地面点提取出来，然后计算非地面点的法向量，并对其进行聚类，得到道路可行驶区域。最后，使用PCL可视化库将结果显示出来。

需要注意的是，上面的代码只是一个简单的示例，实际应用中还需要根据具体情况进行参数调整和算法优化。