本文主要是介绍RoadLib---这两年值得一看的建图定位项目，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
0. 简介
好久都没有看到比较条理清晰的开源项目了，这个项目目前看来有作为记忆行车这类比较火的项目的前提。虽然特征比较少，但是作为建图和定位来说还是基本可以胜任的。这一篇文章我们将着重来看一下整个代码的逻辑，相关的代码注释以及详细内容已经在Github上开源了。由于作者能力有限，欢迎大家一起来讨论
轻量级道路特征地图（开源了）
1. RoadLib之roadlib.h
用于道路检测和地图构建的模块，主要利用Eigen和OpenCV库来处理相机数据和图像数据，实现道路特征的识别和管理。代码的核心功能包括传感器配置、道路实例补丁的定义与处理、帧与地图的管理和融合。
首先，代码定义了一个SensorConfig结构体，用于存储传感器的配置信息，例如相机配置、平滑处理的参数、patch（补丁）相关参数和建图参数等。这些配置参数对于处理和分析传感器数据至关重要，确保数据在处理过程中的一致性和精确性。
其次，代码定义了RoadInstancePatch类，这个类表示道路实例的补丁。每个补丁包含多个属性，例如ID、道路类型、关联帧ID、边界框的四个点和不确定性距离、线相关参数、原始点的坐标等。这个类的主要作用是存储和管理每个检测到的道路特征，并提供计算边界框高度、宽度和方向的方法。
接着，代码定义了RoadInstancePatchFrame类，这个类对应每一帧中的所有道路信息。它包含帧的唯一ID、时间戳、旋转矩阵和平移向量，以及存储不同类型补丁的映射。这个类的主要功能是将图像帧中的道路特征转换为地图帧中的特征。
然后，代码定义了RoadInstancePatchMap类，这个类用于将每一帧的信息存储并归纳到地图中。它包含存储不同类型补丁的映射、参考向量、队列中的姿势、时间戳等。这个类提供了多种方法，例如添加帧、合并补丁、清理地图、保存和加载地图、构建KD树进行地图匹配等。
#pragma once
#include <Eigen/Core>
#include <Eigen/Dense>
#include <Eigen/SVD>
#include<Eigen/StdVector>
#include <opencv2/opencv.hpp>
#include <opencv2/core/eigen.hpp>
#include <opencv2/flann.hpp>#include <iostream>
#include <filesystem>
#include <chrono>
#include <unordered_map>
#include <iomanip>
#include <set>
#include <random>
#include "gv_utils.h"
#include "ipm_processer.h"
#include "utils.hpp"
#include "gviewer.h"using namespace Eigen;
using namespace std;/// @brief 这个是roadlib当中用于配制的一些参数
struct SensorConfig
{
public:SensorConfig(string path);SensorConfig() {};
public:gv::CameraConfig cam;//用于存储相机配置信息int pose_smooth_window = 20;//平滑窗口大小，用于姿态平滑bool need_smooth = true;//是否需要进行平滑处理double large_slope_thresold = 1.5;// 大斜率阈值double t_start;//起始时间double t_end;//结束时间int patch_min_size = 50;//patch最小尺寸double patch_dashed_min_h = 1.35;//虚线patch最小高度double patch_dashed_max_h = 10.0;//虚线patch最大高度double patch_dashed_max_dist = 12.0;//虚线patch最大距离double patch_guide_min_h = 0.0;//引导线patch最小高度double patch_guide_max_h = 1000.0;//引导线patch最大高度double patch_guide_max_dist = 20.0;//引导线patch最大距离double patch_solid_max_dist = 15.0;// 实线patch最大距离double patch_stop_max_dist = 12.0;//停止线patch最大距离int mapping_step = 10;//建图步长double mapping_patch_freeze_distance = 10.0;//patch冻结距离double mapping_line_freeze_distance = 10.0;//线冻结距离double mapping_line_freeze_max_length = 50.0;//线冻结最大长度double mapping_line_cluster_max_dist = 1.0;//线簇最大距离double mapping_line_cluster_max_across_dist1 = 1.0;//线簇最大横向距离1double mapping_line_cluster_max_across_dist2 = 0.4;//线簇最大横向距离2double mapping_line_cluster_max_theta = 10.0;//线簇最大角度int localization_max_windowsize = 100;//定位最大窗口大小int localization_force_last_n_frames = 2;// 定位强制使用最后n帧int localization_every_n_frames = 5;//定位每隔n帧执行一次int localization_min_keyframe_dist = 1.0;//定位最小关键帧距离int localization_max_strict_match_dist = 1.0;//定位最大严格匹配距离int localization_solid_sample_interval = 3.0;//定位实线采样间隔bool enable_vis_image = true;bool enable_vis_3d = true;
};extern gviewer viewer;
extern vector<VisualizedInstance> vis_instances;
extern std::normal_distribution<double> noise_distribution;
extern std::default_random_engine random_engine;enum PatchType { EMPTY = -1, SOLID = 0, DASHED = 1, GUIDE = 2, ZEBRA = 3, STOP = 4 };
/// @brief 将灰度值转换为patch类型
/// @param gray 灰度值
inline PatchType gray2class(int gray)
{if (gray == 2) return PatchType::DASHED;else if (gray == 3) return PatchType::GUIDE;else if (gray == 4) return PatchType::ZEBRA;else if (gray == 5) return PatchType::STOP;else if (gray > 0) return PatchType::SOLID;else return PatchType::EMPTY;
}
/// @brief 将patch类型转换为灰度值
/// @param PatchType patch类型
inline string PatchType2str(PatchType PatchType)
{if (PatchType == PatchType::DASHED) return "dashed";else if (PatchType == PatchType::GUIDE) return "guide";else if (PatchType == PatchType::ZEBRA) return "zebra";else if (PatchType == PatchType::SOLID) return "solid";else if (PatchType == PatchType::STOP) return "stop";else if (PatchType == PatchType::EMPTY) return "empty";else return "unknown";
}/// @brief 车道实例补丁，这个是给出车道实例的一些信息
class RoadInstancePatch
{
public:static long long next_id;//生成下一个实例的idEIGEN_MAKE_ALIGNED_OPERATOR_NEWpublic://** ID/Classlong long id;//实例的唯一标识符PatchType road_class;//表示道路类型的枚举变量map<PatchType, int> road_class_count;//存储不同道路类型数量的映射//** Flagsbool line_valid;bool merged = false;bool frozen = false;bool valid_add_to_map = false;bool out_range = false;long long frame_id;//关联的帧id// 关联RoadInstancePatchMap::timestamps和queued_poses// 对于patch实例->linked_frames[0]// 对于线实例->linked_frames[i], i = 0, ..., line_points_metric.size()vector<vector<long long>> linked_frames; //存储与该实例相关的帧id的向量//** Bounding box related// Main parameters for patch-like instances.// // p3----p2// | |// | |// p0 --- p1//Eigen::Vector3d b_point[4]; // 用于表示边界框的四个点，图像帧中的坐标Eigen::Vector3d b_point_metric[4]; // 用于表示边界框的四个点，在地图帧中的坐标double b_unc_dist[4]; // 边界框四个点的距离不确定性//** Line related// Main parameters for line-like instances.Eigen::VectorXd line_koef;//用于表示线的参数vector<Eigen::Vector3d, Eigen::aligned_allocator<Eigen::Vector3d>> line_points;//线的点集，在图像帧中的坐标，aligned_allocator是为了保证内存对齐vector<Eigen::Vector3d, Eigen::aligned_allocator<Eigen::Vector3d>> line_points_metric;//线的点集，在地图帧中的坐标vector<Eigen::Matrix3d, Eigen::aligned_allocator<Eigen::Matrix3d>> line_points_uncertainty;//线点的不确定性矩阵//** Raw points related (image frame and metric frame)// General attributes.// 1) Image framedouble top, left, width, height;//边界框的位置和尺寸Eigen::Vector3d mean;Eigen::Matrix3d cov;Eigen::Vector3d direction;double eigen_value[3];vector<Vector3d, Eigen::aligned_allocator<Eigen::Vector3d>> points;//原始点的坐标// 2) Body/map frameEigen::Vector3d mean_metric;//在地图帧中的均值vector<Vector3d, Eigen::aligned_allocator<Eigen::Vector3d>> points_metric;//原始点在地图帧中的坐标Eigen::Matrix3d mean_uncertainty;double percept_distance = 10000;//感知距离public:RoadInstancePatch(){id = next_id++;for (int ii = 0; ii < 4; ii++){b_point[ii].setZero();b_point_metric[ii].setZero();}}// Height of bounding box.double h() const;// Width of bounding box.double w() const;// Direction of bounding box/line.Eigen::Vector3d d() const;};/// @brief 车道实例补丁帧，对应了一个帧当中所有的车道信息
class RoadInstancePatchFrame
{
public:static long long next_id;//生成每个RoadInstancePatchFrame对象的唯一idEIGEN_MAKE_ALIGNED_OPERATOR_NEW
public:long long id;//每个RoadInstancePatchFrame对象的唯一iddouble time;Eigen::Matrix3d R; // 旋转矩阵，表示世界坐标系到车体坐标系的变换Eigen::Vector3d t; // 平移向量，表示世界坐标系到车体坐标系的变换map<PatchType, vector<shared_ptr<RoadInstancePatch>>> patches;//存储不同类型补丁的映射，每种类型对应一个补丁对象的共享指针向量。
public:RoadInstancePatchFrame(){id = next_id++;//生成唯一id}// Calculate metric-scale properties of the patches.// Image frame -> body frame.int generateMetricPatches(const SensorConfig &config, const gv::IPMProcesser &ipm);
};/// @brief 车道实例补丁地图，这里会将每一帧的信息存储归纳到地图中
class RoadInstancePatchMap
{
public:EIGEN_MAKE_ALIGNED_OPERATOR_NEWmap<PatchType, vector<shared_ptr<RoadInstancePatch>>> patches;//存储不同类型补丁的映射，每种类型对应一个补丁对象的共享指针向量。Eigen::Vector3d ref;//参考向量。map<long long, pair<Matrix3d, Vector3d>> queued_poses;//队列中的姿势map<long long, double> timestamps;
public:int addFrame(const RoadInstancePatchFrame & frame);// Merge patches in the current map.// mode 0 : incremental mapping; mode 1: map merging/map checking// For mode 0, Rwv and twv are used to determine active instances and freeze old instances.// For mode 1, Rwv and twv are useless. Just use dummy values. (To be improvedint mergePatches(const SensorConfig &config, const int mode, const Eigen::Matrix3d &Rwv = Eigen::Matrix3d::Identity(), const Eigen::Vector3d & twv = Eigen::Vector3d::Zero());// Simply stacking the patches from two maps.// The patches wouldn't be merged.int mergeMap(const RoadInstancePatchMap& road_map_other);// Clear the map.int clearMap();// Unfreeze the pathes (for further merging).int unfreeze();// Integrity checking.int cleanMap();// Save/load functions.// Notice that only metric-scale properties are saved.int saveMapToFileBinaryRaw(string filename);int loadMapFromFileBinaryRaw(string filename);// Build KDtree for map matching.int buildKDTree();// Instance-level nearest matching.map<PatchType, vector<pair<int, int>>> mapMatch(const SensorConfig &config, RoadInstancePatchFrame &frame, int mode = 0); // mode : 0(normal), 1(strict)// Line segment matching (for measurement construction).vector<pair<int, int>> getLineMatch(const SensorConfig &config, RoadInstancePatchFrame &frame, PatchType road_class,int frame_line_count, int map_line_count, int mode =0);// Geo-register the map elements based on linked frames.// Function 'mergePatches' should be called later for consistency.int geoRegister(const Trajectory& new_traj,vector<VisualizedInstance>& lines_vis);public:map<long long, double> ignore_frame_ids; // frame_id - distance threshold
private:};/*** @brief Merge the line instance cluster.* @param lines_in The raw line cluser.* @param line_est The merged line instance.** @return Success flag.*/
extern int LineCluster2SingleLine(const PatchType road_class, const vector<shared_ptr<RoadInstancePatch>>& lines_in, shared_ptr<RoadInstancePatch>& line_est, Eigen::Matrix3d Rwv = Eigen::Matrix3d::Identity());/*** @brief Use the semantic IPM image to generate the road marking instances.* @param config The raw line cluser.* @param ipm IPM processor with up-to-date camera-ground parameters.* @param ipm_raw RGB IPM image.* @param ipm_class Semantic IPM image. (label = 0,1,2,3,4,5, other)** @return Success flag.*/
extern RoadInstancePatchFrame generateInstancePatch(const SensorConfig& config, const gv::IPMProcesser& ipm, const cv::Mat& ipm_raw, const cv::Mat& ipm_class);/*** @brief Calculate the uncertainty of the element on the IPM image based on the pixel coordinates (uI, vI).* @param uI, vI Pixel coordinates on the IPM image.** @return Uncertainty matrix.*/
extern Matrix3d calcUncertainty(const gv::IPMProcesser& ipm, double uI, double vI);extern int PointCloud2Curve2D(const vector<Vector3d, Eigen::aligned_allocator<Eigen::Vector3d>>& points, int dim, VectorXd& K);extern int LabelClustering(const cv::Mat& ipm_class, cv::Mat& ipm_label, cv::Mat& stats, cv::Mat& centroids);
2. RoadLib之roadlib.cpp
该代码主要用于处理道路图像数据，通过标签聚类、计算不确定性和生成道路实例补丁，实现道路特征的检测与管理。以下是对代码核心内容的概括：
-
初始化与导入依赖：代码使用了Eigen和OpenCV库进行矩阵运算和图像处理，并导入了一些自定义的头文件和命名空间。通过
#include "roadlib.h"来导入自定义的函数和结构体。 -
标签聚类函数
LabelClustering：该函数对输入的语义图像进行聚类，计算每个聚类的统计信息（如面积和中心点），并将结果存储在输出矩阵中。它通过OpenCV的connectedComponentsWithStats函数实现语义标签的聚类，并处理了不同语义类别的特殊情况，如停车线。 -
不确定性计算函数
calcUncertainty：该函数根据IPM（Inverse Perspective Mapping）图像处理配置，计算图像像素点的不确定性。它考虑了像素误差、俯仰角误差和高度误差，通过矩阵运算得到一个3x3的不确定性矩阵。 -
生成道路实例补丁
generateInstancePatch：该函数针对每一帧图像，使用聚类结果生成道路实例补丁。它初始化了一个RoadInstancePatchFrame对象，包含多个补丁类型（如实线、虚线、引导线等），并计算每个补丁的特征值（如均值、协方差、特征向量等）。函数通过标签聚类结果和原始IPM图像，将像素点归类到相应的补丁中，并进行特征提取和不确定性计算。 -
道路实例补丁框架
RoadInstancePatchFrame：该类用于管理一帧图像中的所有道路补丁信息。通过generateMetricPatches函数，将图像帧中的补丁信息转换到度量空间，并根据传感器配置和IPM处理器对象，计算每个补丁的度量特征和感知距离。 -
道路实例补丁
RoadInstancePatch：该类用于存储和管理单个道路补丁的信息，包括补丁ID、道路类型、边界框点、线特征、原始点和度量点等。提供了计算补丁高度、宽度和方向的方法，并使用Eigen库进行矩阵运算和特征值分解。 -
传感器配置
SensorConfig：该结构体从配置文件读取相机和传感器相关的参数，如IPM图像尺寸、分辨率、平滑窗口、补丁最小尺寸和距离等。通过解析配置文件，初始化相机配置和传感器参数，为后续的图像处理和特征提取提供依据。
#include "roadlib.h"long long RoadInstancePatch::next_id = 0;
long long RoadInstancePatchFrame::next_id = 0;vector<RoadInstancePatch> patches_temp;
/// @brief 对图像中的语义标签进行聚类，并计算每个聚类的统计信息（如面积和中心点）
/// @param ipm_semantic 输入的语义图像
/// @param ipm_label 输出的标签图像
/// @param stats 输出的统计信息，包含每个聚类的面积、边界框等
/// @param centroids 输出的聚类中心点
/// @return
int LabelClustering(const cv::Mat& ipm_semantic, cv::Mat& ipm_label, cv::Mat& stats, cv::Mat& centroids)
{cv::Mat ipm_label_stop, stats_stop, centroids_stop;cv::Mat ipm_semantic_temp;ipm_semantic.copyTo(ipm_semantic_temp);cv::Mat ipm_semantic_stop = cv::Mat::zeros(ipm_semantic.size(), CV_8UC1);ipm_semantic_stop.setTo(255, ipm_semantic == 5);//将所有值为5的像素设置为255，以便单独处理停车线ipm_semantic_temp.setTo(0, ipm_semantic == 5);//将所有值为5的像素设置为0，以便排除不需要的语义类别// https://blog.csdn.net/qq_40784418/article/details/106023288：stats：表示每一个标记的统计信息，是一个5列的矩阵，每一行对应每个连通区域的外接矩形的x、y、width、height和面积，示例如下： 0 0 720 720 291805cv::connectedComponentsWithStats(ipm_semantic_temp, ipm_label, stats, centroids, 4, CV_16U);//对除停车线外的语义类别进行聚类，得到另一个标签图像、统计信息和中心点cv::connectedComponentsWithStats(ipm_semantic_stop, ipm_label_stop, stats_stop, centroids_stop, 4, CV_16U);//对停车线进行聚类，得到另一个标签图像、统计信息和中心点cv::Mat stats_all, centroids_all;if (stats_stop.rows > 1)//如果停车线聚类的数量大于1{cv::vconcat(stats, stats_stop(cv::Rect(0, 1, stats_stop.cols, stats_stop.rows - 1)), stats);//将停车线聚类的统计信息拼接到所有聚类的统计信息中cv::vconcat(centroids, centroids_stop(cv::Rect(0, 1, centroids_stop.cols, centroids_stop.rows - 1)), centroids);//将停车线聚类的中心点拼接到所有聚类的中心点中}for (int i = 1; i < stats_stop.rows; i++)//将停车线聚类的标签值设置为停车线的标签值，然后将第二个标签图像的标签映射到第一个标签图像的索引上{ipm_label.setTo(stats.rows - stats_stop.rows + i, ipm_label_stop == i);}return 0;
}
/// @brief 计算了图像处理过程中的不确定性，通常用于估计图像重建或特征提取的误差
/// @param ipm IPM预处理信息，包含图像处理的配置和参数
/// @param uI 图像中的像素坐标u，是横坐标
/// @param vI 图像中的像素坐标v，是纵坐标
/// @return
Matrix3d calcUncertainty(const gv::IPMProcesser& ipm, double uI, double vI)
{double fx = ipm._fx;double fy = ipm._fy;double reso_temp = ipm._config.IPM_RESO;double d = ipm._config.cg.getH();Eigen::Matrix3d K_IPM_i_d;K_IPM_i_d << reso_temp, 0.0, -ipm._config.IPM_WIDTH * reso_temp / 2,0.0, reso_temp, -(ipm._config.IPM_HEIGHT - 1) * reso_temp,0.0, 0.0, d;Eigen::Matrix3d K_IPM = K_IPM_i_d.inverse() * d;//给出IPM矩阵Vector3d xyI, xy;double xI, yI, x, y;Eigen::MatrixXd F, F1, F2, F3;xyI = K_IPM.inverse() * Vector3d(uI, vI, 1);//将像素坐标转换为归一化坐标xyIxI = xyI(0); yI = xyI(1);y = -1 / yI; x = xI * y;//将归一化坐标转换为像素坐标// Pixel error.F1 = (Eigen::MatrixXd(2, 2) << d / reso_temp, 0, 0, d / reso_temp).finished();//给出像素误差的F矩阵F2 = (Eigen::MatrixXd(2, 2) << 1 / y, -x / y / y, 0, 1 / y / y).finished();F3 = (Eigen::MatrixXd(2, 2) << 1 / fx, 0, 0, 1 / fy).finished();F = F1 * F2 * F3;//计算像素误差的F矩阵MatrixXd var_pixel = (Eigen::MatrixXd(2, 2) << 2, 0, 0, 2).finished();var_pixel = var_pixel * var_pixel;Matrix2d var_pixel_error = F * var_pixel * F.transpose(); //计算像素误差的协方差矩阵// 计算俯仰角误差的协方差矩阵F1 = (Eigen::MatrixXd(2, 2) << d / reso_temp, 0, 0, d / reso_temp).finished();F2 = (Eigen::MatrixXd(2, 1) << x / y / y, (1 + y * y) / (y * y)).finished();F = F1 * F2;double var_pitch = 0.3 / 57.3;var_pitch = var_pitch * var_pitch;Matrix2d var_pitch_error = F * var_pitch * F.transpose(); // (in pixels)// 计算高度误差的协方差矩阵F = (Eigen::MatrixXd(2, 1) << xI / reso_temp, yI / reso_temp).finished();double var_D = 0.05;var_D = var_D * var_D;Matrix2d var_D_error = F * var_D * F.transpose(); // (in pixels)Matrix3d uncertainty = Matrix3d::Identity(3, 3);uncertainty.block(0, 0, 2, 2) = (var_pixel_error + var_pitch_error + var_D_error) * (reso_temp * reso_temp);uncertainty(2, 2) = pow((0.5 * sqrt(uncertainty(1, 1))), 2);//将这些误差矩阵组合成一个3x3的不确定性矩阵return uncertainty;
}
/// @brief 生成度量空间中的patch，这个是针对一帧的处理
/// @param config 传感器配置信息的引用
/// @param ipm gv::IPMProcesser对象的引用
/// @param ipm_raw 包含原始IPM图像的OpenCV矩阵
/// @param ipm_class 包含IPM类别信息的OpenCV矩阵
/// @return
RoadInstancePatchFrame generateInstancePatch(const SensorConfig& config, const gv::IPMProcesser& ipm, const cv::Mat& ipm_raw, const cv::Mat& ipm_class)
{cv::Mat ipm_instance, stats, centroids;LabelClustering(ipm_class, ipm_instance, stats, centroids);//对ipm_class进行标签聚类，得到ipm_instance、stats和centroids// 初始化RoadInstancePatchFrame对象，对应一帧当中的元素特征RoadInstancePatchFrame frame;frame.patches[PatchType::SOLID] = vector<shared_ptr<RoadInstancePatch>>();frame.patches[PatchType::DASHED] = vector<shared_ptr<RoadInstancePatch>>();frame.patches[PatchType::GUIDE] = vector<shared_ptr<RoadInstancePatch>>();frame.patches[PatchType::ZEBRA] = vector<shared_ptr<RoadInstancePatch>>();frame.patches[PatchType::STOP] = vector<shared_ptr<RoadInstancePatch>>();patches_temp = vector<RoadInstancePatch>(centroids.rows);//初始化对应patch信息vector<int> patches_count(centroids.rows);for (int i = 0; i < centroids.rows; i++){patches_temp[i].points.resize(stats.at<int>(i, cv::CC_STAT_AREA));//对应mat下输出的统计信息，设置patch的点数patches_temp[i].top = stats.at<int>(i, cv::CC_STAT_TOP);patches_temp[i].left = stats.at<int>(i, cv::CC_STAT_LEFT);patches_temp[i].width = stats.at<int>(i, cv::CC_STAT_WIDTH);patches_temp[i].height = stats.at<int>(i, cv::CC_STAT_HEIGHT);}// Count every patch.uint16_t cur_label;for (int i = 0; i < ipm_instance.rows; i++){for (int j = 0; j < ipm_instance.cols; j++)//遍历ipm_instance，根据标签将像素点添加到对应的patches_temp中{cur_label = ipm_instance.at<uint16_t>(i, j);//获取当前像素点的标签patches_temp[cur_label].points[patches_count[cur_label]] = Eigen::Vector3d(j, i, 1);//将像素点添加到对应的patch中patches_temp[cur_label].road_class_count[gray2class(ipm_class.at<uchar>(i, j))]++;//统计每个patch中的语义类别if (cur_label > 0)//判断是否在边界上{if (i + 1 >= ipm_instance.rows || j <= 0 || j >= ipm_instance.cols - 1 ||ipm_raw.at<cv::Vec3b>(i + 1, j) == cv::Vec3b(0, 0, 0) ||ipm_raw.at<cv::Vec3b>(i + 1, j - 1) == cv::Vec3b(0, 0, 0) ||ipm_raw.at<cv::Vec3b>(i + 1, j + 1) == cv::Vec3b(0, 0, 0)){patches_temp[cur_label].out_range = true;//如果在边界上，将out_range设置为true}}patches_count[cur_label]++;//访问下一个像素点}}for (int i = 0; i < patches_temp.size(); i++){assert(patches_temp[i].points.size() == patches_count[i]);//检查patch的点数是否和统计信息中的点数一致}// 清除冗余的补丁auto it = patches_temp.begin();while (it != patches_temp.end()) {int max_class_count = 0;PatchType max_class = PatchType::EMPTY;for (auto class_it : it->road_class_count)//遍历patch中的语义类别{if (class_it.second > max_class_count)//找到出现次数最多的语义类别{max_class_count = class_it.second;max_class = class_it.first;}}it->road_class = max_class;//将patch的road_class设置为出现次数最多的语义类别if (max_class == PatchType::EMPTY)//如果没有语义类别，将patch删除{it = patches_temp.erase(it);continue;}bool out_range = false;if (it->points.size() < config.patch_min_size|| (it->road_class == PatchType::DASHED && out_range)|| (it->road_class == PatchType::GUIDE && out_range))//如果patch的点数小于最小点数，或者是虚线且在边界上，或者是引导线且在边界上，将patch删除{it = patches_temp.erase(it);continue;}it++;}// 计算每个补丁的特征值，如均值、协方差、特征向量等for (int i = 0; i < patches_temp.size(); i++){auto& patch = patches_temp[i];patch.mean.setZero();patch.cov.setZero();Eigen::Vector3d vector_temp;for (int j = 0; j < patch.points.size(); j++){patch.mean += patch.points[j];}patch.mean /= patch.points.size();//对于每个patch，将其所有点相加得到均值for (int j = 0; j < patch.points.size(); j++){vector_temp = patch.points[j] - patch.mean;patch.cov += vector_temp * vector_temp.transpose();//计算每个点与均值的差值，更新协方差矩阵}patch.cov /= patch.points.size();JacobiSVD<Eigen::MatrixXd> svd(patch.cov, ComputeThinU | ComputeThinV);//使用JacobiSVD分解协方差矩阵，得到特征向量和特征值Matrix3d V = svd.matrixV(), U = svd.matrixU();Matrix3d S = U.inverse() * patch.cov * V.transpose().inverse();//A=UΣV'，其中U和V是两组正交单位向量，都是是对角阵，是表示奇异值patch.eigen_value[0] = sqrt(S(0, 0));patch.eigen_value[1] = sqrt(S(1, 1));patch.eigen_value[2] = sqrt(S(2, 2));patch.direction = U.block(0, 0, 3, 1);//根据特征值计算patch的方向patch.mean_uncertainty = calcUncertainty(ipm, patch.mean(0), patch.mean(1));//平均不确定性if (patch.direction(1) < 0) patch.direction *= -1;//如在特征向量计算过程中，特征向量的方向是无法确定的，只有其方向的相对性是可确定的。所以在实际应用中，为了保持一致性，可以将特征向量的方向调整为统一的方向。//特征值比例大于2且road_class为SOLID或STOPif (patch.eigen_value[0] / patch.eigen_value[1] > 2&& (patches_temp[i].road_class == PatchType::SOLID || patches_temp[i].road_class == PatchType::STOP)){auto points_direction = patch.points;//获取点云double direction_angle = atan2(patch.direction(1), patch.direction(0)) + M_PI / 2; //根据特征向量计算方向角度Matrix3d direction_rotation;direction_rotation << cos(direction_angle), sin(direction_angle), 0,-sin(direction_angle), cos(direction_angle), 0,0, 0, 1;//根据方向角度计算旋转矩阵for (int i = 0; i < points_direction.size(); i++){points_direction[i] = direction_rotation * (patch.points[i] - patch.mean) + patch.mean;//将点云旋转到特征向量的方向}// 将线状点云转换为骨架点if (PointCloud2Curve2D(points_direction, 4, patch.line_koef) < 0)patch.line_valid = false;else{patch.line_valid = true;double yy, xx;Vector3d pt_img;double min_uncertainty = 1e9;double reso_temp = ipm._config.IPM_RESO;for (int ll = (int)ceil(-patch.eigen_value[0] * 2 / (0.5 / reso_temp));ll < patch.eigen_value[0] * 2 / (0.5 / reso_temp); ll++)//从patch的中心点开始，沿着特征向量的方向，计算线状点云的不确定性{yy = patch.mean(1) + ll * 0.5 / reso_temp;//计算线状点云的y坐标xx = 0.0;for (int i = 0; i < patch.line_koef.rows(); i++)xx += pow(yy, i) * patch.line_koef(i);//计算线状点云的x坐标，这个是曲线信息pt_img = direction_rotation.transpose() * (Vector3d(xx, yy, 1) - patch.mean) + patch.mean;//将线状点云旋转到特征向量的方向if (pt_img(1) > (patch.top + patch.height)) continue;double valid_distance = 20;if (patches_temp[i].road_class == PatchType::SOLID) valid_distance = config.patch_solid_max_dist;//根据road_class的不同，设置不同的有效距离else if (patches_temp[i].road_class == PatchType::STOP) valid_distance = config.patch_stop_max_dist;if ((ipm._config.IPM_HEIGHT - pt_img(1)) * reso_temp < valid_distance)//如果点云的y坐标小于有效距离，将点云添加到patch的线状点云中{patch.line_points.push_back(pt_img);patch.line_points_uncertainty.push_back(calcUncertainty(ipm, pt_img(0), pt_img(1)));min_uncertainty = min(min_uncertainty,sqrt(patch.line_points_uncertainty.back()(0, 0) + patch.line_points_uncertainty.back()(1, 1)));//计算线状点云的不确定性}}if (patch.line_points.size() < 2 || min_uncertainty > 1.0)patch.line_valid = false;}}elsepatch.line_valid = false;}// We collect nearby lane line segments to determine the direction of patch-like markings.// This could be improved.vector<pair<Vector3d, Vector3d>> direction_field;for (int i = 0; i < patches_temp.size(); i++){auto& patch = patches_temp[i];if (patch.road_class == PatchType::DASHED)direction_field.push_back(make_pair(patch.mean, patch.direction));if (patches_temp[i].road_class == PatchType::SOLID && patch.line_valid)for (int ii = 0; ii < patch.line_points.size() - 1; ii++)direction_field.push_back(make_pair(patch.line_points[ii], patch.line_points[ii + 1] - patch.line_points[ii]));//将线状点云的方向添加到方向场中}//计算边界框。使用局部方向场确定补丁式标记的方向for (int i = 0; i < patches_temp.size(); i++){auto patch = make_shared<RoadInstancePatch>(patches_temp[i]);if (patch->road_class == PatchType::GUIDE || patch->road_class == PatchType::DASHED)//如果是引导线或虚线{double min_dist = 999999;int min_ii = -1;for (int ii = 0; ii < direction_field.size(); ii++)//计算patch的方向{double dist = (direction_field[ii].first - patch->mean).norm();//计算patch的中心点和方向场中的点的距禙if (dist < min_dist){min_dist = dist;min_ii = ii;}}if (min_dist < 10000)//如果距离小于10000，计算patch的边界框{Eigen::Vector3d b_direction = direction_field[min_ii].second / direction_field[min_ii].second.norm();//计算patch的方向if (b_direction(1) < 0) b_direction *= -1;//如果方向向下，将方向调整为向上double bb= -1e9; Vector3d tt_support;double tt = 1e9; Vector3d bb_support;double ll = 1e9; Vector3d ll_support;double rr = -1e9; Vector3d rr_support;double direction_angle = -atan2(b_direction(1), b_direction(0)) + M_PI / 2;Matrix3d direction_rotation;direction_rotation << cos(direction_angle), sin(direction_angle), 0,-sin(direction_angle), cos(direction_angle), 0,0, 0, 1;//根据方向角度计算旋转矩阵for (int jj = 0; jj < patch->points.size(); jj++){Vector3d pt = direction_rotation.transpose() * (patch->points[jj] - patch->mean);//根据最小距离的方向，将patch的点云旋转到特征向量的方向if (pt.x() < ll) { ll = pt.x(); ll_support = patch->points[jj]; }//计算patch的边界框if (pt.x() > rr) { rr = pt.x(); rr_support = patch->points[jj]; }if (pt.y() < tt) { tt = pt.y(); tt_support = patch->points[jj]; }if (pt.y() > bb) { bb = pt.y(); bb_support = patch->points[jj]; }//if (patch->road_class == PatchType::GUIDE)// std::cerr << patch->points[jj].transpose() << std::endl;}patch->b_point[0] = direction_rotation * Vector3d(ll, bb, 0) + patch->mean;//将patch的边界框旋转到特征向量的方向patch->b_point[1] = direction_rotation * Vector3d(rr, bb, 0) + patch->mean;patch->b_point[2] = direction_rotation * Vector3d(rr, tt, 0) + patch->mean;patch->b_point[3] = direction_rotation * Vector3d(ll, tt, 0) + patch->mean;Eigen::Matrix3d P0 = calcUncertainty(ipm, ll_support(0), ll_support(1));//计算patch的不确定性Eigen::Matrix3d P1 = calcUncertainty(ipm, rr_support(0), rr_support(1));Eigen::Matrix3d P2 = calcUncertainty(ipm, bb_support(0), bb_support(1));Eigen::Matrix3d P3 = calcUncertainty(ipm, tt_support(0), tt_support(1));
#ifdef DEBUG//cv::Mat mm(1000, 1000, CV_8UC3);//cv::circle(mm, cv::Point2f(ll_support(0), ll_support(1)), 5, cv::Scalar(255, 0, 0));//cv::circle(mm, cv::Point2f(rr_support(0), rr_support(1)), 5, cv::Scalar(0, 255, 0));//cv::circle(mm, cv::Point2f(bb_support(0), bb_support(1)), 5, cv::Scalar(0, 0, 255));//cv::circle(mm, cv::Point2f(tt_support(0), tt_support(1)), 5, cv::Scalar(255, 255, 0));drawUncertainty(mm, ll_support, P0/config.IPM_RESO/config.IPM_RESO);drawUncertainty(mm, rr_support, P1/config.IPM_RESO/config.IPM_RESO);drawUncertainty(mm, bb_support, P2/config.IPM_RESO/config.IPM_RESO);drawUncertainty(mm, tt_support, P3/config.IPM_RESO/config.IPM_RESO);//drawUncertainty_dist(mm, ll_support, P0 / config.IPM_RESO / config.IPM_RESO, Vector3d(-b_direction(1), b_direction(0), b_direction(2)));//drawUncertainty_dist(mm, rr_support, P1 / config.IPM_RESO / config.IPM_RESO, Vector3d(-b_direction(1), b_direction(0), b_direction(2)));//drawUncertainty_dist(mm, bb_support, P2 / config.IPM_RESO / config.IPM_RESO, b_direction);//drawUncertainty_dist(mm, tt_support, P3 / config.IPM_RESO / config.IPM_RESO, b_direction);//cv::imshow("mm_debug", mm);//cv::waitKey(0);
#endifVector3d b_direction_t = Vector3d(-b_direction(1), b_direction(0), b_direction(2));patch->b_unc_dist[0] = sqrt(b_direction.transpose() * P2 * b_direction);patch->b_unc_dist[1] = sqrt(b_direction_t.transpose() * P1 * b_direction_t);patch->b_unc_dist[2] = sqrt(b_direction.transpose() * P3 * b_direction);patch->b_unc_dist[3] = sqrt(b_direction_t.transpose() * P0 * b_direction_t);if (patch->out_range){patch->b_unc_dist[0] *=100;patch->b_unc_dist[1] *=100;patch->b_unc_dist[3] *=100;}}else{continue;}}if (patches_temp[i].road_class == PatchType::SOLID ||patches_temp[i].road_class == PatchType::DASHED ||patches_temp[i].road_class == PatchType::GUIDE ||patches_temp[i].road_class == PatchType::ZEBRA ||patches_temp[i].road_class == PatchType::STOP){frame.patches[patches_temp[i].road_class].push_back(patch);}}return frame;
}/// @brief 道路实例信息处理函数
/// @param config 配置文件
/// @param ipm IPM图
/// @return
int RoadInstancePatchFrame::generateMetricPatches(const SensorConfig& config, const gv::IPMProcesser& ipm)
{auto cam = ipm._config;//首先使用 IPM 处理器对象获取摄像头参数 camfor (auto iter_class : patches){for (int i = 0; i < iter_class.second.size(); i++){auto& patch = iter_class.second[i];patch->points_metric.resize(patch->points.size());for (int j = 0; j < patch->points.size(); j++){patch->points_metric[j] = cam.tic+ cam.Ric * ipm.IPM2Metric(cv::Point2f(patch->points[j].x(), patch->points[j].y()));//将patch的点云转换到度量空间}if (patch->line_valid){patch->line_points_metric.resize(patch->line_points.size());for (int j = 0; j < patch->line_points.size(); j++){patch->line_points_metric[j] = cam.tic+ cam.Ric * ipm.IPM2Metric(cv::Point2f(patch->line_points[j].x(), patch->line_points[j].y()));}}if (patch->road_class == PatchType::DASHED || patch->road_class == PatchType::GUIDE){for (int ii = 0; ii < 4; ii++){patch->b_point_metric[ii] = cam.tic+ cam.Ric * ipm.IPM2Metric(cv::Point2f(patch->b_point[ii].x(), patch->b_point[ii].y()));}}patch->mean_metric.setZero();Eigen::Vector3d vector_temp;for (int j = 0; j < patch->points_metric.size(); j++){patch->mean_metric += patch->points_metric[j];}patch->mean_metric /= patch->points_metric.size();//计算补丁的平均度量patch->percept_distance = patch->mean_metric(1);//计算补丁的感知距离if (iter_class.first == PatchType::DASHED&& patch->h() > config.patch_dashed_min_h&& patch->h() < config.patch_dashed_max_h&& patch->mean_metric(1) < config.patch_dashed_max_dist)patch->valid_add_to_map = true;else if (iter_class.first == PatchType::GUIDE&& patch->h() > config.patch_guide_min_h&& patch->h() < config.patch_guide_max_h&& patch->mean_metric(1) < config.patch_guide_max_dist)patch->valid_add_to_map = true;else if (patch->line_valid)patch->valid_add_to_map = true;}}return 0;
}
/// @brief 计算补丁的高度
/// @return
double RoadInstancePatch::h() const
{return (b_point_metric[2] - b_point_metric[1]).norm();
}
/// @brief 计算补丁的宽度
/// @return
double RoadInstancePatch::w() const
{return (b_point_metric[1] - b_point_metric[0]).norm();
}
/// @brief 根据不同类型的补丁计算其方向
/// @return
Eigen::Vector3d RoadInstancePatch::d() const
{if (road_class == PatchType::DASHED || road_class == PatchType::GUIDE) return (b_point_metric[2] - b_point_metric[1]).normalized();else if (road_class == PatchType::SOLID || road_class == PatchType::STOP){assert(line_points_metric.size() >= 2);return (line_points_metric.back() - line_points_metric.front()).normalized();}elsethrow exception();
}
/// @brief 从文件路径读取摄像头和传感器相关的配置参数
/// @param path 文件路径
SensorConfig::SensorConfig(string path)
{gv::CameraConfig conf;cv::FileStorage fs_config(path, cv::FileStorage::READ);conf.IPM_HEIGHT = (int)fs_config["IPM_HEIGHT"];conf.IPM_WIDTH = (int)fs_config["IPM_WIDTH"];conf.IPM_RESO = (double)fs_config["IPM_RESO"];double t_start = (double)fs_config["t_start"];double t_end = (double)fs_config["t_end"];conf.RAW_RESIZE = 1;int pn = path.find_last_of('/');std::string configPath = path.substr(0, pn);conf.camera = camodocal::CameraFactory::instance()->generateCameraFromYamlFile(configPath + '/' + (string)fs_config["cam0_calib"]);conf.cg = gv::CameraGroundGeometry((double)fs_config["cg_alpha"], (double)fs_config["cg_theta"], (double)fs_config["cg_h"]);cv::Mat cv_T;fs_config["body_T_cam0"] >> cv_T;Eigen::Matrix4d Tic;cv::cv2eigen(cv_T, Tic);conf.Ric = Tic.block<3, 3>(0, 0);conf.tic = Tic.block<3, 1>(0, 3);conf.Tic.setIdentity();conf.Tic.block<3, 3>(0, 0) = conf.Ric;conf.Tic.block<3, 1>(0, 3) = conf.tic;this->cam = conf;this->pose_smooth_window = (int)fs_config["pose_smooth_window"];this->need_smooth = (bool)(int)fs_config["need_smooth"];this->large_slope_thresold = (double)fs_config["large_slope_thresold"];this->patch_min_size = (int)fs_config["patch.min_size"];this->patch_dashed_min_h = (double)fs_config["patch.dashed_min_h"];this->patch_dashed_max_h = (double)fs_config["patch.dashed_max_h"];this->patch_dashed_max_dist = (double)fs_config["patch.dashed_max_dist"];this->patch_guide_min_h = (double)fs_config["patch.guide_min_h"];this->patch_guide_max_h = (double)fs_config["patch.guide_max_h"];this->patch_guide_max_dist = (double)fs_config["patch.guide_max_dist"];this->patch_solid_max_dist = (double)fs_config["patch.solid_max_dist"];this->patch_stop_max_dist = (double)fs_config["patch.stop_max_dist"];this->mapping_step = (int)fs_config["mapping.step"];this->mapping_patch_freeze_distance = (double)fs_config["mapping.patch_freeze_distance"];this->mapping_line_freeze_distance = (double)fs_config["mapping.line_freeze_distance"];this->mapping_line_freeze_max_length = (double)fs_config["mapping.line_freeze_max_length"];this->mapping_line_cluster_max_dist = (double)fs_config["mapping.line_cluster_max_dist"];this->mapping_line_cluster_max_across_dist1 = (double)fs_config["mapping.line_cluster_max_across_dist1"];this->mapping_line_cluster_max_across_dist2 = (double)fs_config["mapping.line_cluster_max_across_dist2"];this->mapping_line_cluster_max_theta = (double)fs_config["mapping.line_cluster_max_theta"];this->localization_every_n_frames = (int)fs_config["localization.every_n_frames"];this->localization_force_last_n_frames = (int)fs_config["localization.force_last_n_frames"];this->localization_max_windowsize = (int)fs_config["localization.max_windowsize"];this->localization_min_keyframe_dist = (int)fs_config["localization.min_keyframe_dist"];this->localization_max_strict_match_dist = (int)fs_config["localization.max_strict_match_dist"];this->localization_solid_sample_interval = (int)fs_config["localization.solid_sample_interval"];this->enable_vis_image = (bool)(int)fs_config["enable_vis_image"];this->enable_vis_3d = (bool)(int)fs_config["enable_vis_3d"];this->t_start = (double)fs_config["t_start"];this->t_end = (double)fs_config["t_end"];}
3. RoadLib之roadlib_optim.cpp
这段代码实现了两个主要功能：将三维点云数据拟合成二维曲线，以及将多个线段聚类成一条直线。以下是对代码核心内容的概括：
点云拟合成二维曲线 (PointCloud2Curve2D)
该函数将三维点云数据拟合成二维曲线。具体步骤如下：
- 初始化矩阵和向量：创建矩阵
U和向量Y，其中U的每行代表一个点在不同维度上的幂次方值，Y是目标值（即点的x坐标）。 - 填充数据：遍历输入的三维点云数据，将点的y坐标的幂次方值填入
U，将x坐标填入Y。 - 最小二乘法求解：使用最小二乘法求解系数向量
K，使得U * K ≈ Y。 - 计算误差：计算拟合误差，如果误差小于阈值则返回成功，否则返回失败。
线段聚类成直线 (LineCluster2SingleLine)
该函数将多个线段聚类成一条直线。具体步骤如下：
- 预处理：对输入的线段进行预处理，去除空线段。
- 计算所有点的均值和协方差：将所有线段的点聚合到一个向量中，计算这些点的均值和协方差。
- 特征值分解：对协方差矩阵进行特征值分解，获取主方向。
- 坐标变换：将所有点变换到新的坐标系中，使得主方向与车辆航向角对齐。
- 线段拟合：在新坐标系下，对线段进行参数化拟合，优化过程中考虑点之间的距离和连续性，最终计算出线段的估计值。
- 计算协方差矩阵：使用稀疏矩阵和线性方程求解器计算线段点的不确定性协方差矩阵。
- 生成最终的线段实例：将拟合的线段点和不确定性添加到最终的线段实例中，计算线段的均值。
#include "roadlib.h"
#include <ceres/ceres.h>
#include <Eigen/Sparse>
/// @brief 将给定的三维点云数据拟合成二维曲线
/// @param points 输入的三维点云数据
/// @param dim 曲线的维度
/// @param K 拟合得到的系数，类型为VectorXd&
/// @return
int PointCloud2Curve2D(const vector<Vector3d, Eigen::aligned_allocator<Eigen::Vector3d>>& points, int dim, VectorXd& K)
{MatrixXd U = MatrixXd::Zero(points.size(), dim);//U是一个矩阵，每行代表一个点在不同维度上的幂次方值VectorXd Y = VectorXd::Zero(points.size());//Y是目标值（即x坐标）for (int i = 0; i < points.size(); i++){for (int j = 0; j < dim; j++)U(i, j) = pow(points[i].y(), j);Y(i) = points[i].x();}K = (U.transpose() * U).inverse() * U.transpose() * Y;//使用最小二乘法求解系数Kdouble res = sqrt(pow((U * K - Y).norm(), 2) / points.size());//得到拟合的误差if (res < 5) return 0;else return -1;
}
/// @brief 将多个线段聚类成一条直线
/// @param road_class 道路类型
/// @param lines_in 输入的线段集合
/// @param line_est 输出的估计直线
/// @param Rwv 旋转矩阵
/// @return
int LineCluster2SingleLine(const PatchType road_class, const vector<shared_ptr<RoadInstancePatch>>& lines_in, shared_ptr<RoadInstancePatch>& line_est, Eigen::Matrix3d Rwv)
{vector<RoadInstancePatch> lines;//对输入的线段进行预处理，去除空线段for (int i = 0; i < lines_in.size(); i++){if (lines_in[i]->line_points_metric.size() < 1) continue;lines.push_back(*lines_in[i]);}if (lines_in.size() == 1){line_est = lines_in.front();return 0;}if (lines_in.size() == 0)return -1;vector<Vector3d> all_pts;Eigen::Vector3d vector_temp;Eigen::Vector3d all_mean; all_mean.setZero();Eigen::Matrix3d all_cov; all_cov.setZero();//将所有的点放入一个向量中for (auto& line_seg : lines){for (int i = 0; i < line_seg.line_points_metric.size(); i++){all_pts.push_back(line_seg.line_points_metric[i]);}}//if (merge_mode == 0 && all_pts.size() < 10) return -1;for (int j = 0; j < all_pts.size(); j++){all_mean += all_pts[j];}all_mean /= all_pts.size();//计算所有点的均值for (int j = 0; j < all_pts.size(); j++){vector_temp = all_pts[j] - all_mean;//计算所有点相对于均值的向量all_cov += vector_temp * vector_temp.transpose();//对输入的线段进行预处理，去除空线段}all_cov /= all_pts.size();double eigen_value[3];Eigen::Vector3d direction;JacobiSVD<Eigen::MatrixXd> svd(all_cov, ComputeThinU | ComputeThinV);Matrix3d V = svd.matrixV(), U = svd.matrixU();Matrix3d S = U.inverse() * all_cov * V.transpose().inverse();eigen_value[0] = sqrt(S(0, 0));eigen_value[1] = sqrt(S(1, 1));eigen_value[2] = sqrt(S(2, 2));direction = U.block(0, 0, 3, 1);double yaw = m2att(Rwv).z() * 180 / M_PI+90;//获取yaw角度double direction_angle = atan2(direction(1), direction(0));// 通过特征值分解获取主方向if (fabs(yaw - direction_angle * 180 / M_PI) < 90) {}else{direction_angle += M_PI;}Matrix3d direction_rotation;direction_rotation << cos(direction_angle), sin(direction_angle), 0,-sin(direction_angle), cos(direction_angle), 0,0, 0, 1;//std::cerr << direction_rotation << std::endl;bool is_ref_set = false;Vector3d point_ref;for (auto& line_seg : lines){for (int i = 0; i < line_seg.line_points_metric.size(); i++){if (!is_ref_set){point_ref = line_seg.line_points_metric[i];is_ref_set = true;}line_seg.line_points_metric[i] = direction_rotation * (line_seg.line_points_metric[i] - point_ref);//坐标变换，使得主方向与车辆航向角对齐}}if (isnan(point_ref.x())){std::cerr << "NaN!!" << std::endl;return -1;}double min_x = 1e9;double max_x = -1e9;//计算所有点的x坐标的最大值和最小值for (auto& line_seg : lines){for (int i = 0; i < line_seg.line_points_metric.size(); i++){//std::cerr << line_seg.line_points_metric[i].x() << " ";if (line_seg.line_points_metric[i].x() < min_x) min_x = line_seg.line_points_metric[i].x();if (line_seg.line_points_metric[i].x() > max_x) max_x = line_seg.line_points_metric[i].x();}}//计算x坐标的估计值VectorXd x_est((int)ceil(max_x) - (int)floor(min_x) + 1);for (int i = 0; i < x_est.rows(); i++){x_est(i) = min_x + i;}//x_est(0) = min_x;x_est(x_est.rows() - 1) = max_x;int nq = x_est.rows();//nq为x_est的行数//std::cerr << x_est << std::endl;VectorXd y_est = VectorXd::Zero(nq * 2);MatrixXd BtPB = MatrixXd::Zero(nq * 2, nq * 2);VectorXd BtPl = MatrixXd::Zero(nq * 2, 1);MatrixXd BtPB_obs;//开始迭代优化，在新坐标系下，对线段进行参数化拟合，优化过程中考虑点之间的距离和连续性。for (int i_iter = 0; i_iter < 5; i_iter++){SparseMatrix<double> BtPBs(nq * 2, nq * 2);SparseMatrix<double> BtPls(nq * 2, 1);BtPB = MatrixXd::Zero(nq * 2, nq * 2);//BtPB是一个矩阵，每个元素代表两个点之间的距禒BtPl = MatrixXd::Zero(nq * 2, 1);// Line segments (weighted)double x, y, z; double xn, yn, zn; double x0, y0, z0; double x1, y1, z1;for (int i = 0; i < lines.size(); i++)//对每个线段进行处理{// Point distance (y-axis direction)for (int j = 0; j < lines[i].line_points_metric.size(); j++)//对每个线段的每个点进行处理{x = lines[i].line_points_metric[j].x();y = lines[i].line_points_metric[j].y();z = lines[i].line_points_metric[j].z();int seg_index = -1;for (int m = 0; m < nq - 1; m++) if (x_est(m) < x + 1e-3 && x_est(m + 1) > x - 1e-3) seg_index = m;//找到点所在的线段,需要确保点的平滑性x0 = x_est(seg_index); y0 = y_est(seg_index); z0 = y_est(seg_index + nq);//x0,y0,z0为线段的起点x1 = x_est(seg_index + 1); y1 = y_est(seg_index + 1); z1 = y_est(seg_index + 1 + nq);//x1,y1,z1为线段的终点Eigen::VectorXd l = Eigen::VectorXd::Zero(2); Eigen::MatrixXd B = Eigen::MatrixXd::Zero(2, nq * 2);l(0) = y - (y0 + (y1 - y0) / (x1 - x0) * (x - x0));//l为点到线段在y轴上的投影距离l(1) = z - (z0 + (z1 - z0) / (x1 - x0) * (x - x0));//l为点到线段在z轴上的投影距离B(0, seg_index) = -(1 - (x - x0) / (x1 - x0)); B(0, seg_index + 1) = -(x - x0) / (x1 - x0);//用于存储关于线段的信息B(1, seg_index + nq) = -(1 - (x - x0) / (x1 - x0)); B(1, seg_index + 1 + nq) = -(x - x0) / (x1 - x0);Eigen::MatrixXd BB0(1,2),BB1(1,2);BB0 << -(1 - (x - x0) / (x1 - x0)), -(x - x0) / (x1 - x0);//存储了与B相同的数值BB1 << -(1 - (x - x0) / (x1 - x0)), -(x - x0) / (x1 - x0);double point_error = sqrt(lines[i].line_points_uncertainty[j](0, 0) + lines[i].line_points_uncertainty[j](1, 1)) / sqrt(2.0);//计算点的误差Eigen::MatrixXd P = Eigen::MatrixXd::Identity(2, 2) / pow(point_error, 2);//BtPB = BtPB + B.transpose() * P * B; BtPl = BtPl + B.transpose() * P * l;BtPB.block(seg_index, seg_index, 2, 2) += BB0.transpose()*BB0 / pow(point_error, 2);//计算BtPB,更新雅可比矩阵中的特定块BtPB.block(seg_index+nq, seg_index + nq, 2, 2) += BB1.transpose()*BB1 / pow(point_error, 2);BtPl.segment(seg_index, 2) += BB0.transpose() * l(0) / pow(point_error, 2);//更新梯度向量中的特定部分BtPl.segment(seg_index+nq, 2) += BB1.transpose() * l(1) / pow(point_error, 2);}BtPB_obs = BtPB;}//连续性约束for (int i = 0; i < nq - 2; i++){Eigen::VectorXd l = Eigen::VectorXd::Zero(2); Eigen::MatrixXd B = Eigen::MatrixXd::Zero(2, nq * 2);l(0, 0) = (y_est(i + 1) - y_est(i)) - (y_est(i + 2) - y_est(i + 1));//l为点到线段在y轴上的投影距离l(1, 0) = (y_est(nq + i + 1) - y_est(nq + i)) - (y_est(nq + i + 2) - y_est(nq + i + 1));//l为点到线段在z轴上的投影距离B(0, i) = -1; B(0, i + 1) = 2; B(0, i + 2) = -1;B(1, nq + i) = -1; B(1, nq + i + 1) = 2; B(1, nq + i + 2) = -1;Eigen::MatrixXd BB0 = Eigen::MatrixXd(1, 3); BB0 << -1, 2, -1;//存储了与B相同的数值Eigen::MatrixXd BB1 = Eigen::MatrixXd(1, 3); BB1 << -1, 2, -1;Eigen::MatrixXd P;//Eigen::MatrixXd P = Eigen::MatrixXd::Identity(2, 2)/0.04;if (road_class == PatchType::SOLID)P = Eigen::MatrixXd::Identity(2, 2) / 0.04;else if (road_class == PatchType::STOP)P = Eigen::MatrixXd::Identity(2, 2) / (0.01 * 0.01);//BtPB = BtPB + B.transpose() * P * B; BtPl = BtPl + B.transpose() * P * l;BtPB.block(i, i, 3, 3) += BB0.transpose() * P(0,0) * BB0;//计算BtPB,更新雅可比矩阵中的特定块BtPB.block(nq+i, nq+i, 3, 3) += BB1.transpose() *P(1,1) * BB1;BtPl.segment(i, 3) += BB0.transpose() * P(0, 0) * l(0);BtPl.segment(nq + i, 3) += BB1.transpose() * P(1, 1) * l(1);}BtPBs = BtPB.sparseView();//将BtPB转换为稀疏矩阵BtPls = BtPl.sparseView();SimplicialLDLT< SparseMatrix<double>>solver;solver.compute(BtPBs);Eigen::VectorXd dy = solver.solve(BtPls);//求解线性方程组y_est = y_est - dy;//更新y_est}SparseMatrix<double> BtPBs_obs = (BtPB_obs + MatrixXd::Identity(nq * 2, nq * 2) * 0.00001).sparseView();//将BtPB_obs转换为稀疏矩阵SimplicialLDLT< SparseMatrix<double>>solver;solver.compute(BtPBs_obs);//求解线性方程组SparseMatrix<double> I(BtPB_obs.rows(), BtPB_obs.rows());I.setIdentity();MatrixXd Cov = solver.solve(I).toDense();line_est = make_shared<RoadInstancePatch>();line_est->road_class = lines_in[0]->road_class;int i_start = 0;int i_end = nq - 1;for (int i = i_start; i <= i_end; i++){if (i >= i_start + (i_end-i_start)/2 && Cov(i, i) > 0.35) break;line_est->line_points_metric.push_back(direction_rotation.transpose() * Vector3d(x_est(i), y_est(i), y_est(i + nq)) + point_ref);//将点放入line_est中line_est->line_points_uncertainty.push_back((Eigen::Matrix3d() << Cov(i, i), 0, 0, 0, Cov(i, i), 0, 0, 0, 1).finished());}if (line_est->line_points_metric.size() <= 3)return -1;for (int iii = 0; iii < line_est->line_points_metric.size(); iii++){if (isnan(line_est->line_points_metric[iii].x()))return -1;}line_est->valid_add_to_map = true;line_est->line_valid = true;line_est->merged = true;line_est->mean_metric.setZero();vector_temp.setZero();for (int j = 0; j < line_est->line_points_metric.size(); j++){line_est->mean_metric += line_est->line_points_metric[j];}line_est->mean_metric /= line_est->line_points_metric.size();return 0;}
4. RoadLib之roadlib_map.cpp
这个这个项目当中最核心的算法，实现了对道路实例补丁的管理，包括添加新帧数据、清理、存储和加载补丁数据、构建KD树以进行快速邻近搜索、补丁匹配、合并补丁以及进行地理配准等功能。以下是对代码核心内容的概括：
…详情请参照古月居
这篇关于RoadLib---这两年值得一看的建图定位项目的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
