CMU-Exploration 代码笔记（一）

总揽

仿真中重要的部分就是包含差速车的仿真（包含激光雷达等），地形分析、局部路径规划、路径更踪等；

sensor_scan_generation包

#include <math.h>
#include <time.h>
#include <stdio.h>
#include <stdlib.h>
#include <ros/ros.h>

#include <nav_msgs/Odometry.h>
#include <sensor_msgs/PointCloud2.h>

#include <tf/transform_datatypes.h>
#include <tf/transform_broadcaster.h>

#include <pcl_conversions/pcl_conversions.h>
#include <pcl/point_cloud.h>
#include <pcl/point_types.h>

#include <message_filters/subscriber.h>
#include <message_filters/sync_policies/approximate_time.h>
#include <message_filters/synchronizer.h>

using namespace std;

pcl::PointCloud<pcl::PointXYZ>::Ptr laserCloudIn(new pcl::PointCloud<pcl::PointXYZ>());
pcl::PointCloud<pcl::PointXYZ>::Ptr laserCLoudInSensorFrame(new pcl::PointCloud<pcl::PointXYZ>());

double robotX = 0;
double robotY = 0;
double robotZ = 0;
double roll = 0;
double pitch = 0;
double yaw = 0;

bool newTransformToMap = false;

nav_msgs::Odometry odometryIn;
ros::Publisher *pubOdometryPointer = NULL;
tf::StampedTransform transformToMap;
tf::TransformBroadcaster *tfBroadcasterPointer = NULL;
ros::Publisher pubLaserCloud;

//回调函数
void laserCloudAndOdometryHandler(const nav_msgs::Odometry::ConstPtr& odometry,
                                  const sensor_msgs::PointCloud2ConstPtr& laserCloud2)
{
  laserCloudIn->clear();
  laserCLoudInSensorFrame->clear();

  pcl::fromROSMsg(*laserCloud2, *laserCloudIn);

  odometryIn = *odometry;

  transformToMap.setOrigin(
      tf::Vector3(odometryIn.pose.pose.position.x, odometryIn.pose.pose.position.y, odometryIn.pose.pose.position.z));
  transformToMap.setRotation(tf::Quaternion(odometryIn.pose.pose.orientation.x, odometryIn.pose.pose.orientation.y,
                                            odometryIn.pose.pose.orientation.z, odometryIn.pose.pose.orientation.w));

  int laserCloudInNum = laserCloudIn->points.size();

  pcl::PointXYZ p1;
  tf::Vector3 vec;

  //把所有点转到sensor坐标系上？
  //这里/registered_scan是投影到map坐标系里的
  for (int i = 0; i < laserCloudInNum; i++)
  {
    p1 = laserCloudIn->points[i];
    vec.setX(p1.x);
    vec.setY(p1.y);
    vec.setZ(p1.z);

    vec = transformToMap.inverse() * vec;

    p1.x = vec.x();
    p1.y = vec.y();
    p1.z = vec.z();

    laserCLoudInSensorFrame->points.push_back(p1);
  }
  
  odometryIn.header.stamp = laserCloud2->header.stamp;
  odometryIn.header.frame_id = "map";
  odometryIn.child_frame_id = "sensor_at_scan";
  pubOdometryPointer->publish(odometryIn);

  transformToMap.stamp_ = laserCloud2->header.stamp;
  transformToMap.frame_id_ = "map";
  transformToMap.child_frame_id_ = "sensor_at_scan";
  tfBroadcasterPointer->sendTransform(transformToMap);

  sensor_msgs::PointCloud2 scan_data;
  pcl::toROSMsg(*laserCLoudInSensorFrame, scan_data);
  scan_data.header.stamp = laserCloud2->header.stamp;
  scan_data.header.frame_id = "sensor_at_scan";
  pubLaserCloud.publish(scan_data);
}

int main(int argc, char** argv)
{
  //ros初始化
  ros::init(argc, argv, "sensor_scan");
  ros::NodeHandle nh;
  ros::NodeHandle nhPrivate = ros::NodeHandle("~");

  /************创建message_filters类************
    message_filters类是 ROS（机器人操作系统）中的一个功能包，用于实现多个传感器数据或消息的时间同步。
    可以方便地对不同话题发布的消息进行时间戳的同步，以确保数据在处理时具有一致的时间对齐
    ************************
    即可以直接回调函数对两个及以上话题收到的消息在同一时间处理；
    LOAM/LIOSAM等都是把数据存vector里遍历找时间戳相同的，人工时间对齐
  */
  message_filters::Subscriber<nav_msgs::Odometry> subOdometry;
  message_filters::Subscriber<sensor_msgs::PointCloud2> subLaserCloud;

  /***********创建message_filters类************
   * 
   *  sync_policies::ApproximateTime 方法允许话题之间的时间辍存在一定的偏差
   *  sync_policies::ExactTime 表示必须时间完全一致，可能会导致频率下降
   *  syncPolicy(100) 100是queue size，队列长度，默认为10，
   */
  typedef message_filters::sync_policies::ApproximateTime<nav_msgs::Odometry, sensor_msgs::PointCloud2> syncPolicy;
  typedef message_filters::Synchronizer<syncPolicy> Sync;
  boost::shared_ptr<Sync> sync_;
  subOdometry.subscribe(nh, "/state_estimation", 1);
  subLaserCloud.subscribe(nh, "/registered_scan", 1);
  sync_.reset(new Sync(syncPolicy(100), subOdometry, subLaserCloud));
  sync_->registerCallback(boost::bind(laserCloudAndOdometryHandler, _1, _2));


  //值得学习，ros::Publisher类只有指针pubOdometryPointer是全局变量
  //这样不用创建ros::Publisher的全局变量
  ros::Publisher pubOdometry = nh.advertise<nav_msgs::Odometry> ("/state_estimation_at_scan", 5);
  pubOdometryPointer = &pubOdometry;
  tf::TransformBroadcaster tfBroadcaster;
  tfBroadcasterPointer = &tfBroadcaster;
  pubLaserCloud = nh.advertise<sensor_msgs::PointCloud2>("/sensor_scan", 2);

  ros::spin();

  return 0;
}

这个功能包只是运行了；对自主导航、规划没有太大用处
并没有功能包收/sensor_scan和/state_estimation_at_scan消息

这个功能包的目的是接收map坐标系下的点云数据
然后把这个数据转到sensor坐标系下，猜测是用来检测仿真是否正常的

loam_interface

loam_interface 包，在real_robot应用时主要用来转发odom数据和register_scan数据，因为有些定位是摄像头坐标系下的，所以用这个包来转发