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ndt_matching解读

额外开启一个线程检测并更新地图

void* thread_func(void* args)
{
  ros::NodeHandle nh_map;
  ros::CallbackQueue map_callback_queue;
  nh_map.setCallbackQueue(&map_callback_queue);

  ros::Subscriber map_sub = nh_map.subscribe("points_map", 10, map_callback);
  ros::Rate ros_rate(10);
  while (nh_map.ok())
  {
    map_callback_queue.callAvailable(ros::WallDuration());
    ros_rate.sleep();
  }

“map_callback” 地图回调函数

static void map_callback(const sensor_msgs::PointCloud2::ConstPtr& input)
{
  // if (map_loaded == 0)
  if (points_map_num != input->width) //points_map_num默认为0,如果和收到的地图数据大小不一致代表地图更新了
  {
    std::cout << "Update points_map." << std::endl;

    points_map_num = input->width;

    // Convert the data type(from sensor_msgs to pcl).
    pcl::fromROSMsg(*input, map);

    if (_use_local_transform == true)//默认为false,不知道是干嘛的
    {
      tf::TransformListener local_transform_listener;
      try
      {
        ros::Time now = ros::Time(0);
        local_transform_listener.waitForTransform("/map", "/world", now, ros::Duration(10.0));
        local_transform_listener.lookupTransform("/map", "world", now, local_transform);
      }
      catch (tf::TransformException& ex)
      {
        ROS_ERROR("%s", ex.what());
      }

      pcl_ros::transformPointCloud(map, map, local_transform.inverse());
    }

    pcl::PointCloud<pcl::PointXYZ>::Ptr map_ptr(new pcl::PointCloud<pcl::PointXYZ>(map));

    // Setting point cloud to be aligned to.
    /*
    MethodType::PCL_GENERIC == 0
    MethodType::PCL_ANH == 1
    MethodType::PCL_ANH_GPU == 2
    MethodType::PCL_OPENMP == 3 
    */
    if (_method_type == MethodType::PCL_GENERIC) //_method_type默认为0
    {
      pcl::NormalDistributionsTransform<pcl::PointXYZ, pcl::PointXYZ> new_ndt;//创建了带默认参数的NDT算法对象。
      pcl::PointCloud<pcl::PointXYZ>::Ptr output_cloud(new pcl::PointCloud<pcl::PointXYZ>);
      new_ndt.setResolution(ndt_res);               //设置NDT网格网格结构的分辨率(voxelgridcovariance)
      new_ndt.setInputTarget(map_ptr);             //目标点云
      new_ndt.setMaximumIterations(max_iter);       //这个参数控制了优化过程的最大迭代次数。一般来说,在达到最大迭代次数之前程序就会先达到epsilon阈值而终止。添加最大迭代次数的限制能够增加程序鲁棒性,阻止了它在错误的方向运行过长时间
      new_ndt.setStepSize(step_size);                //为more-thuente线搜索设置最大步长
      new_ndt.setTransformationEpsilon(trans_eps);  //为终止条件设置最小转换差异

      new_ndt.align(*output_cloud, Eigen::Matrix4f::Identity());//点云配准,变换后的点云保存在output cloud里,之后打印出配准分数。分数通过计算output cloud与target cloud对应的最近点欧式距离的平方和得到,得分越小说明匹配效果越好。

      pthread_mutex_lock(&mutex);
      ndt = new_ndt;
      pthread_mutex_unlock(&mutex);
    }
    else if (_method_type == MethodType::PCL_ANH)
    {
      cpu::NormalDistributionsTransform<pcl::PointXYZ, pcl::PointXYZ> new_anh_ndt;
      new_anh_ndt.setResolution(ndt_res);
      new_anh_ndt.setInputTarget(map_ptr);
      new_anh_ndt.setMaximumIterations(max_iter);
      new_anh_ndt.setStepSize(step_size);
      new_anh_ndt.setTransformationEpsilon(trans_eps);

      pcl::PointCloud<pcl::PointXYZ>::Ptr dummy_scan_ptr(new pcl::PointCloud<pcl::PointXYZ>());
      pcl::PointXYZ dummy_point;
      dummy_scan_ptr->push_back(dummy_point);
      new_anh_ndt.setInputSource(dummy_scan_ptr);

      new_anh_ndt.align(Eigen::Matrix4f::Identity());

      pthread_mutex_lock(&mutex);
      anh_ndt = new_anh_ndt;
      pthread_mutex_unlock(&mutex);
    }
#ifdef CUDA_FOUND
    else if (_method_type == MethodType::PCL_ANH_GPU)
    {
      std::shared_ptr<gpu::GNormalDistributionsTransform> new_anh_gpu_ndt_ptr =
          std::make_shared<gpu::GNormalDistributionsTransform>();
      new_anh_gpu_ndt_ptr->setResolution(ndt_res);
      new_anh_gpu_ndt_ptr->setInputTarget(map_ptr);
      new_anh_gpu_ndt_ptr->setMaximumIterations(max_iter);
      new_anh_gpu_ndt_ptr->setStepSize(step_size);
      new_anh_gpu_ndt_ptr->setTransformationEpsilon(trans_eps);

      pcl::PointCloud<pcl::PointXYZ>::Ptr dummy_scan_ptr(new pcl::PointCloud<pcl::PointXYZ>());
      pcl::PointXYZ dummy_point;
      dummy_scan_ptr->push_back(dummy_point);
      new_anh_gpu_ndt_ptr->setInputSource(dummy_scan_ptr);

      new_anh_gpu_ndt_ptr->align(Eigen::Matrix4f::Identity());

      pthread_mutex_lock(&mutex);
      anh_gpu_ndt_ptr = new_anh_gpu_ndt_ptr;
      pthread_mutex_unlock(&mutex);
    }
#endif
#ifdef USE_PCL_OPENMP
    else if (_method_type == MethodType::PCL_OPENMP)
    {
      pcl_omp::NormalDistributionsTransform<pcl::PointXYZ, pcl::PointXYZ> new_omp_ndt;
      pcl::PointCloud<pcl::PointXYZ>::Ptr output_cloud(new pcl::PointCloud<pcl::PointXYZ>);
      new_omp_ndt.setResolution(ndt_res);
      new_omp_ndt.setInputTarget(map_ptr);
      new_omp_ndt.setMaximumIterations(max_iter);
      new_omp_ndt.setStepSize(step_size);
      new_omp_ndt.setTransformationEpsilon(trans_eps);

      new_omp_ndt.align(*output_cloud, Eigen::Matrix4f::Identity());

      pthread_mutex_lock(&mutex);
      omp_ndt = new_omp_ndt;
      pthread_mutex_unlock(&mutex);
    }
#endif
    map_loaded = 1;
  }
}

“param_callback” 默认参数初始化配置

static void param_callback(const autoware_config_msgs::ConfigNDT::ConstPtr& input)
{
  //传入的参数是否与默认参数相同,不同则重置标志位
  if (_use_gnss != input->init_pos_gnss)
  {
    init_pos_set = 0;
  }
  else if (_use_gnss == 0 &&
           (initial_pose.x != input->x || initial_pose.y != input->y || initial_pose.z != input->z ||
            initial_pose.roll != input->roll || initial_pose.pitch != input->pitch || initial_pose.yaw != input->yaw))
  {
    init_pos_set = 0;
  }

  _use_gnss = input->init_pos_gnss;

  // Setting parameters
  //判断传入的参数和默认的参数是否相同,不同则改为传入的参数
  if (input->resolution != ndt_res)
  {
    ndt_res = input->resolution;

    if (_method_type == MethodType::PCL_GENERIC)
      ndt.setResolution(ndt_res);
    else if (_method_type == MethodType::PCL_ANH)
      anh_ndt.setResolution(ndt_res);
#ifdef CUDA_FOUND
    else if (_method_type == MethodType::PCL_ANH_GPU)
      anh_gpu_ndt_ptr->setResolution(ndt_res);
#endif
#ifdef USE_PCL_OPENMP
    else if (_method_type == MethodType::PCL_OPENMP)
      omp_ndt.setResolution(ndt_res);
#endif
  }

  if (input->step_size != step_size)
  {
    step_size = input->step_size;

    if (_method_type == MethodType::PCL_GENERIC)
      ndt.setStepSize(step_size);
    else if (_method_type == MethodType::PCL_ANH)
      anh_ndt.setStepSize(step_size);
#ifdef CUDA_FOUND
    else if (_method_type == MethodType::PCL_ANH_GPU)
      anh_gpu_ndt_ptr->setStepSize(step_size);
#endif
#ifdef USE_PCL_OPENMP
    else if (_method_type == MethodType::PCL_OPENMP)
      omp_ndt.setStepSize(ndt_res);
#endif
  }

  if (input->trans_epsilon != trans_eps)
  {
    trans_eps = input->trans_epsilon;

    if (_method_type == MethodType::PCL_GENERIC)
      ndt.setTransformationEpsilon(trans_eps);
    else if (_method_type == MethodType::PCL_ANH)
      anh_ndt.setTransformationEpsilon(trans_eps);
#ifdef CUDA_FOUND
    else if (_method_type == MethodType::PCL_ANH_GPU)
      anh_gpu_ndt_ptr->setTransformationEpsilon(trans_eps);
#endif
#ifdef USE_PCL_OPENMP
    else if (_method_type == MethodType::PCL_OPENMP)
      omp_ndt.setTransformationEpsilon(ndt_res);
#endif
  }

  if (input->max_iterations != max_iter)
  {
    max_iter = input->max_iterations;

    if (_method_type == MethodType::PCL_GENERIC)
      ndt.setMaximumIterations(max_iter);
    else if (_method_type == MethodType::PCL_ANH)
      anh_ndt.setMaximumIterations(max_iter);
#ifdef CUDA_FOUND
    else if (_method_type == MethodType::PCL_ANH_GPU)
      anh_gpu_ndt_ptr->setMaximumIterations(max_iter);
#endif
#ifdef USE_PCL_OPENMP
    else if (_method_type == MethodType::PCL_OPENMP)
      omp_ndt.setMaximumIterations(ndt_res);
#endif
  }
  //如果不开启gnss且初始化标志位为0
  if (_use_gnss == 0 && init_pos_set == 0)
  {
    initial_pose.x = input->x;
    initial_pose.y = input->y;
    initial_pose.z = input->z;
    initial_pose.roll = input->roll;
    initial_pose.pitch = input->pitch;
    initial_pose.yaw = input->yaw;

    if (_use_local_transform == true)
    {
      tf::Vector3 v(input->x, input->y, input->z);
      tf::Quaternion q;
      q.setRPY(input->roll, input->pitch, input->yaw);
      tf::Transform transform(q, v);
      initial_pose.x = (local_transform.inverse() * transform).getOrigin().getX();
      initial_pose.y = (local_transform.inverse() * transform).getOrigin().getY();
      initial_pose.z = (local_transform.inverse() * transform).getOrigin().getZ();

      tf::Matrix3x3 m(q);
      m.getRPY(initial_pose.roll, initial_pose.pitch, initial_pose.yaw);

      std::cout << "initial_pose.x: " << initial_pose.x << std::endl;
      std::cout << "initial_pose.y: " << initial_pose.y << std::endl;
      std::cout << "initial_pose.z: " << initial_pose.z << std::endl;
      std::cout << "initial_pose.roll: " << initial_pose.roll << std::endl;
      std::cout << "initial_pose.pitch: " << initial_pose.pitch << std::endl;
      std::cout << "initial_pose.yaw: " << initial_pose.yaw << std::endl;
    }

    // Setting position and posture for the first time.
    /*把传入的参数设置为初始化参数
    localizer_pose 定位器
    previous_pose  以前的数据
    current_pose   当前的数据
    */
    localizer_pose.x = initial_pose.x;
    localizer_pose.y = initial_pose.y;
    localizer_pose.z = initial_pose.z;
    localizer_pose.roll = initial_pose.roll;
    localizer_pose.pitch = initial_pose.pitch;
    localizer_pose.yaw = initial_pose.yaw;

    previous_pose.x = initial_pose.x;
    previous_pose.y = initial_pose.y;
    previous_pose.z = initial_pose.z;
    previous_pose.roll = initial_pose.roll;
    previous_pose.pitch = initial_pose.pitch;
    previous_pose.yaw = initial_pose.yaw;

    current_pose.x = initial_pose.x;
    current_pose.y = initial_pose.y;
    current_pose.z = initial_pose.z;
    current_pose.roll = initial_pose.roll;
    current_pose.pitch = initial_pose.pitch;
    current_pose.yaw = initial_pose.yaw;

    current_velocity = 0;
    current_velocity_x = 0;
    current_velocity_y = 0;
    current_velocity_z = 0;
    angular_velocity = 0;

    current_pose_imu.x = 0;
    current_pose_imu.y = 0;
    current_pose_imu.z = 0;
    current_pose_imu.roll = 0;
    current_pose_imu.pitch = 0;
    current_pose_imu.yaw = 0;

    current_velocity_imu_x = current_velocity_x;
    current_velocity_imu_y = current_velocity_y;
    current_velocity_imu_z = current_velocity_z;
    printf("genggai--------------1");
    init_pos_set = 1;
  }
}

"points_callback" ndt匹配定位模块

static void points_callback(const sensor_msgs::PointCloud2::ConstPtr& input)
{
  if (map_loaded == 1 && init_pos_set == 1) //map_loaded和init_pos_set默认都为0,收到地图后map_loaded为1,参数初始化或者设置位置之后init_pos_set为1
  {
    matching_start = std::chrono::system_clock::now();//获取当前系统时间

    static tf::TransformBroadcaster br;//tf广播
    tf::Transform transform;
    /*
    predict 速度积分预估
    previous 上一时刻
    ndt 正态分布匹配出来的base_link位置
    current 当前时刻最终定位
    localizer 正态分布匹配出来的lidar位置
    */
    tf::Quaternion predict_q, ndt_q, current_q, localizer_q;

    pcl::PointXYZ p;
    pcl::PointCloud<pcl::PointXYZ> filtered_scan;

    ros::Time current_scan_time = input->header.stamp;
    static ros::Time previous_scan_time = current_scan_time;

    pcl::fromROSMsg(*input, filtered_scan);
    pcl::PointCloud<pcl::PointXYZ>::Ptr filtered_scan_ptr(new pcl::PointCloud<pcl::PointXYZ>(filtered_scan));
    int scan_points_num = filtered_scan_ptr->size();
    //初始化矩阵
    Eigen::Matrix4f t(Eigen::Matrix4f::Identity());   // base_link
    Eigen::Matrix4f t2(Eigen::Matrix4f::Identity());  // localizer

    std::chrono::time_point<std::chrono::system_clock> align_start, align_end, getFitnessScore_start,
        getFitnessScore_end;
    static double align_time, getFitnessScore_time = 0.0;

    pthread_mutex_lock(&mutex);
    //加载配准点云
    if (_method_type == MethodType::PCL_GENERIC)
      ndt.setInputSource(filtered_scan_ptr);
    else if (_method_type == MethodType::PCL_ANH)
      anh_ndt.setInputSource(filtered_scan_ptr);
#ifdef CUDA_FOUND
    else if (_method_type == MethodType::PCL_ANH_GPU)
      anh_gpu_ndt_ptr->setInputSource(filtered_scan_ptr);
#endif
#ifdef USE_PCL_OPENMP
    else if (_method_type == MethodType::PCL_OPENMP)
      omp_ndt.setInputSource(filtered_scan_ptr);
#endif

    // Guess the initial gross estimation of the transformation
    // 当前帧的雷达时间和上一帧的雷达时间间隔
    double diff_time = (current_scan_time - previous_scan_time).toSec();
    //速度积分,上一时刻速度*间隔时间
    if (_offset == "linear")
    {
      offset_x = current_velocity_x * diff_time;
      offset_y = current_velocity_y * diff_time;
      offset_z = current_velocity_z * diff_time;
      offset_yaw = angular_velocity * diff_time;
    }
    else if (_offset == "quadratic")
    {
      offset_x = (current_velocity_x + current_accel_x * diff_time) * diff_time;
      offset_y = (current_velocity_y + current_accel_y * diff_time) * diff_time;
      offset_z = current_velocity_z * diff_time;
      offset_yaw = angular_velocity * diff_time;
    }
    else if (_offset == "zero")
    {
      offset_x = 0.0;
      offset_y = 0.0;
      offset_z = 0.0;
      offset_yaw = 0.0;
    }

    predict_pose.x = previous_pose.x + offset_x;
    predict_pose.y = previous_pose.y + offset_y;
    predict_pose.z = previous_pose.z + offset_z;
    predict_pose.roll = previous_pose.roll;
    predict_pose.pitch = previous_pose.pitch;
    predict_pose.yaw = previous_pose.yaw + offset_yaw;

    if (_use_imu == true && _use_odom == true)
      imu_odom_calc(current_scan_time);
    if (_use_imu == true && _use_odom == false)
      imu_calc(current_scan_time);
    if (_use_imu == false && _use_odom == true)
      odom_calc(current_scan_time);

    pose predict_pose_for_ndt;
    if (_use_imu == true && _use_odom == true)
      predict_pose_for_ndt = predict_pose_imu_odom;
    else if (_use_imu == true && _use_odom == false)
      predict_pose_for_ndt = predict_pose_imu;
    else if (_use_imu == false && _use_odom == true)
      predict_pose_for_ndt = predict_pose_odom;
    else
      predict_pose_for_ndt = predict_pose;//最终选择
    //提供了点云配准变换,由之前的(x,y,z,roll,pitch,yaw)求出旋转矩阵
    Eigen::Translation3f init_translation(predict_pose_for_ndt.x, predict_pose_for_ndt.y, predict_pose_for_ndt.z);
    Eigen::AngleAxisf init_rotation_x(predict_pose_for_ndt.roll, Eigen::Vector3f::UnitX());
    Eigen::AngleAxisf init_rotation_y(predict_pose_for_ndt.pitch, Eigen::Vector3f::UnitY());
    Eigen::AngleAxisf init_rotation_z(predict_pose_for_ndt.yaw, Eigen::Vector3f::UnitZ());
    Eigen::Matrix4f init_guess = (init_translation * init_rotation_z * init_rotation_y * init_rotation_x) * tf_btol;

    pcl::PointCloud<pcl::PointXYZ>::Ptr output_cloud(new pcl::PointCloud<pcl::PointXYZ>);

    if (_method_type == MethodType::PCL_GENERIC)//默认_method_type为0
    {
      align_start = std::chrono::system_clock::now();
      ndt.align(*output_cloud, init_guess);//进行配准打分和输出
      align_end = std::chrono::system_clock::now();

      has_converged = ndt.hasConverged();//正态分布收敛值

      t = ndt.getFinalTransformation();//得出的变换矩阵
      iteration = ndt.getFinalNumIteration();//获取计算对齐所需的迭代次数

      getFitnessScore_start = std::chrono::system_clock::now();
      fitness_score = ndt.getFitnessScore();//匹配分数
      getFitnessScore_end = std::chrono::system_clock::now();

      trans_probability = ndt.getTransformationProbability();//转化概率
    }
    else if (_method_type == MethodType::PCL_ANH)
    {
      align_start = std::chrono::system_clock::now();
      anh_ndt.align(init_guess);
      align_end = std::chrono::system_clock::now();

      has_converged = anh_ndt.hasConverged();

      t = anh_ndt.getFinalTransformation();
      iteration = anh_ndt.getFinalNumIteration();

      getFitnessScore_start = std::chrono::system_clock::now();
      fitness_score = anh_ndt.getFitnessScore();
      getFitnessScore_end = std::chrono::system_clock::now();

      trans_probability = anh_ndt.getTransformationProbability();
    }
#ifdef CUDA_FOUND
    else if (_method_type == MethodType::PCL_ANH_GPU)
    {
      align_start = std::chrono::system_clock::now();
      anh_gpu_ndt_ptr->align(init_guess);
      align_end = std::chrono::system_clock::now();

      has_converged = anh_gpu_ndt_ptr->hasConverged();

      t = anh_gpu_ndt_ptr->getFinalTransformation();
      iteration = anh_gpu_ndt_ptr->getFinalNumIteration();

      getFitnessScore_start = std::chrono::system_clock::now();
      fitness_score = anh_gpu_ndt_ptr->getFitnessScore();
      getFitnessScore_end = std::chrono::system_clock::now();

      trans_probability = anh_gpu_ndt_ptr->getTransformationProbability();
    }
#endif
#ifdef USE_PCL_OPENMP
    else if (_method_type == MethodType::PCL_OPENMP)
    {
      align_start = std::chrono::system_clock::now();
      omp_ndt.align(*output_cloud, init_guess);
      align_end = std::chrono::system_clock::now();

      has_converged = omp_ndt.hasConverged();

      t = omp_ndt.getFinalTransformation();
      iteration = omp_ndt.getFinalNumIteration();

      getFitnessScore_start = std::chrono::system_clock::now();
      fitness_score = omp_ndt.getFitnessScore();
      getFitnessScore_end = std::chrono::system_clock::now();

      trans_probability = omp_ndt.getTransformationProbability();
    }
#endif
    //配准打分的时间
    align_time = std::chrono::duration_cast<std::chrono::microseconds>(align_end - align_start).count() / 1000.0;

    t2 = t * tf_btol.inverse();//tf_btol.inverse():机器人到雷达的静态变换矩阵   t:雷达到点云的变换矩阵
    //获取匹配分数的时间
    getFitnessScore_time =
        std::chrono::duration_cast<std::chrono::microseconds>(getFitnessScore_end - getFitnessScore_start).count() /
        1000.0;

    pthread_mutex_unlock(&mutex);

    tf::Matrix3x3 mat_l;  // localizer
    mat_l.setValue(static_cast<double>(t(0, 0)), static_cast<double>(t(0, 1)), static_cast<double>(t(0, 2)),
                   static_cast<double>(t(1, 0)), static_cast<double>(t(1, 1)), static_cast<double>(t(1, 2)),
                   static_cast<double>(t(2, 0)), static_cast<double>(t(2, 1)), static_cast<double>(t(2, 2)));

    // Update localizer_pose
    localizer_pose.x = t(0, 3);
    localizer_pose.y = t(1, 3);
    localizer_pose.z = t(2, 3);
    mat_l.getRPY(localizer_pose.roll, localizer_pose.pitch, localizer_pose.yaw, 1);

    tf::Matrix3x3 mat_b;  // base_link
    mat_b.setValue(static_cast<double>(t2(0, 0)), static_cast<double>(t2(0, 1)), static_cast<double>(t2(0, 2)),
                   static_cast<double>(t2(1, 0)), static_cast<double>(t2(1, 1)), static_cast<double>(t2(1, 2)),
                   static_cast<double>(t2(2, 0)), static_cast<double>(t2(2, 1)), static_cast<double>(t2(2, 2)));

    // Update ndt_pose
    ndt_pose.x = t2(0, 3);
    ndt_pose.y = t2(1, 3);
    ndt_pose.z = t2(2, 3);
    mat_b.getRPY(ndt_pose.roll, ndt_pose.pitch, ndt_pose.yaw, 1);

    // Calculate the difference between ndt_pose and predict_pose
    // 计算ndt_pose和predict_pose之间的差异
    //predict_pose_for_ndt为这次位置的速度积分值
    predict_pose_error = sqrt((ndt_pose.x - predict_pose_for_ndt.x) * (ndt_pose.x - predict_pose_for_ndt.x) +
                              (ndt_pose.y - predict_pose_for_ndt.y) * (ndt_pose.y - predict_pose_for_ndt.y) +
                              (ndt_pose.z - predict_pose_for_ndt.z) * (ndt_pose.z - predict_pose_for_ndt.z));

    if (predict_pose_error <= PREDICT_POSE_THRESHOLD)//PREDICT_POSE_THRESHOLD默认值为0.5
    {
      use_predict_pose = 0;
    }
    else
    {
      use_predict_pose = 1;
    }
    use_predict_pose = 0;

    if (use_predict_pose == 0)//如果为0,说明速度积分值和计算匹配值相近,则说明定位没有跳跃,设当前位置为计算匹配值
    {
      current_pose.x = ndt_pose.x;
      current_pose.y = ndt_pose.y;
      current_pose.z = ndt_pose.z;
      current_pose.roll = ndt_pose.roll;
      current_pose.pitch = ndt_pose.pitch;
      current_pose.yaw = ndt_pose.yaw;
    }
    else//如果为1,说明预估值和计算匹配值相差教导,则说明定位出现了跳跃,设当前位置为速度积分值
    {
      current_pose.x = predict_pose_for_ndt.x;
      current_pose.y = predict_pose_for_ndt.y;
      current_pose.z = predict_pose_for_ndt.z;
      current_pose.roll = predict_pose_for_ndt.roll;
      current_pose.pitch = predict_pose_for_ndt.pitch;
      current_pose.yaw = predict_pose_for_ndt.yaw;
    }

    // Compute the velocity and acceleration
    //计算速度和加速度
    //位置差异 = 当前位置 - 上一位置
    diff_x = current_pose.x - previous_pose.x;
    diff_y = current_pose.y - previous_pose.y;
    diff_z = current_pose.z - previous_pose.z;
    diff_yaw = calcDiffForRadian(current_pose.yaw, previous_pose.yaw);
    diff = sqrt(diff_x * diff_x + diff_y * diff_y + diff_z * diff_z);
    //当前速度 = 位置差异/时间间隔
    current_velocity = (diff_time > 0) ? (diff / diff_time) : 0;
    current_velocity_x = (diff_time > 0) ? (diff_x / diff_time) : 0;
    current_velocity_y = (diff_time > 0) ? (diff_y / diff_time) : 0;
    current_velocity_z = (diff_time > 0) ? (diff_z / diff_time) : 0;
    angular_velocity = (diff_time > 0) ? (diff_yaw / diff_time) : 0;

    current_pose_imu.x = current_pose.x;
    current_pose_imu.y = current_pose.y;
    current_pose_imu.z = current_pose.z;
    current_pose_imu.roll = current_pose.roll;
    current_pose_imu.pitch = current_pose.pitch;
    current_pose_imu.yaw = current_pose.yaw;

    current_velocity_imu_x = current_velocity_x;
    current_velocity_imu_y = current_velocity_y;
    current_velocity_imu_z = current_velocity_z;

    current_pose_odom.x = current_pose.x;
    current_pose_odom.y = current_pose.y;
    current_pose_odom.z = current_pose.z;
    current_pose_odom.roll = current_pose.roll;
    current_pose_odom.pitch = current_pose.pitch;
    current_pose_odom.yaw = current_pose.yaw;

    current_pose_imu_odom.x = current_pose.x;
    current_pose_imu_odom.y = current_pose.y;
    current_pose_imu_odom.z = current_pose.z;
    current_pose_imu_odom.roll = current_pose.roll;
    current_pose_imu_odom.pitch = current_pose.pitch;
    current_pose_imu_odom.yaw = current_pose.yaw;
    //当前速度平滑值 = (当前速度+上一时间速度+上上一时间速度)/3.0
    current_velocity_smooth = (current_velocity + previous_velocity + previous_previous_velocity) / 3.0;
    if (current_velocity_smooth < 0.2)
    {
      current_velocity_smooth = 0.0;
    }
    //当前加速度 = (当前速度 - 上一时刻速度)/时间间隔
    current_accel = (diff_time > 0) ? ((current_velocity - previous_velocity) / diff_time) : 0;
    current_accel_x = (diff_time > 0) ? ((current_velocity_x - previous_velocity_x) / diff_time) : 0;
    current_accel_y = (diff_time > 0) ? ((current_velocity_y - previous_velocity_y) / diff_time) : 0;
    current_accel_z = (diff_time > 0) ? ((current_velocity_z - previous_velocity_z) / diff_time) : 0;
    //估计速度m/s,km/h
    estimated_vel_mps.data = current_velocity;
    estimated_vel_kmph.data = current_velocity * 3.6;

    estimated_vel_mps_pub.publish(estimated_vel_mps);
    estimated_vel_kmph_pub.publish(estimated_vel_kmph);

    // Set values for publishing pose
    //速度积分预估值的姿态
    predict_q.setRPY(predict_pose.roll, predict_pose.pitch, predict_pose.yaw);
    if (_use_local_transform == true)
    {
      tf::Vector3 v(predict_pose.x, predict_pose.y, predict_pose.z);
      tf::Transform transform(predict_q, v);
      predict_pose_msg.header.frame_id = "/map";
      predict_pose_msg.header.stamp = current_scan_time;
      predict_pose_msg.pose.position.x = (local_transform * transform).getOrigin().getX();
      predict_pose_msg.pose.position.y = (local_transform * transform).getOrigin().getY();
      predict_pose_msg.pose.position.z = (local_transform * transform).getOrigin().getZ();
      predict_pose_msg.pose.orientation.x = (local_transform * transform).getRotation().x();
      predict_pose_msg.pose.orientation.y = (local_transform * transform).getRotation().y();
      predict_pose_msg.pose.orientation.z = (local_transform * transform).getRotation().z();
      predict_pose_msg.pose.orientation.w = (local_transform * transform).getRotation().w();
    }
    else //默认为false
    {
      predict_pose_msg.header.frame_id = "/map";
      predict_pose_msg.header.stamp = current_scan_time;
      predict_pose_msg.pose.position.x = predict_pose.x;
      predict_pose_msg.pose.position.y = predict_pose.y;
      predict_pose_msg.pose.position.z = predict_pose.z;
      predict_pose_msg.pose.orientation.x = predict_q.x();
      predict_pose_msg.pose.orientation.y = predict_q.y();
      predict_pose_msg.pose.orientation.z = predict_q.z();
      predict_pose_msg.pose.orientation.w = predict_q.w();
    }

    tf::Quaternion predict_q_imu;
    predict_q_imu.setRPY(predict_pose_imu.roll, predict_pose_imu.pitch, predict_pose_imu.yaw);
    predict_pose_imu_msg.header.frame_id = "map";
    predict_pose_imu_msg.header.stamp = input->header.stamp;
    predict_pose_imu_msg.pose.position.x = predict_pose_imu.x;
    predict_pose_imu_msg.pose.position.y = predict_pose_imu.y;
    predict_pose_imu_msg.pose.position.z = predict_pose_imu.z;
    predict_pose_imu_msg.pose.orientation.x = predict_q_imu.x();
    predict_pose_imu_msg.pose.orientation.y = predict_q_imu.y();
    predict_pose_imu_msg.pose.orientation.z = predict_q_imu.z();
    predict_pose_imu_msg.pose.orientation.w = predict_q_imu.w();
    predict_pose_imu_pub.publish(predict_pose_imu_msg);

    tf::Quaternion predict_q_odom;
    predict_q_odom.setRPY(predict_pose_odom.roll, predict_pose_odom.pitch, predict_pose_odom.yaw);
    predict_pose_odom_msg.header.frame_id = "map";
    predict_pose_odom_msg.header.stamp = input->header.stamp;
    predict_pose_odom_msg.pose.position.x = predict_pose_odom.x;
    predict_pose_odom_msg.pose.position.y = predict_pose_odom.y;
    predict_pose_odom_msg.pose.position.z = predict_pose_odom.z;
    predict_pose_odom_msg.pose.orientation.x = predict_q_odom.x();
    predict_pose_odom_msg.pose.orientation.y = predict_q_odom.y();
    predict_pose_odom_msg.pose.orientation.z = predict_q_odom.z();
    predict_pose_odom_msg.pose.orientation.w = predict_q_odom.w();
    predict_pose_odom_pub.publish(predict_pose_odom_msg);

    tf::Quaternion predict_q_imu_odom;
    predict_q_imu_odom.setRPY(predict_pose_imu_odom.roll, predict_pose_imu_odom.pitch, predict_pose_imu_odom.yaw);
    predict_pose_imu_odom_msg.header.frame_id = "map";
    predict_pose_imu_odom_msg.header.stamp = input->header.stamp;
    predict_pose_imu_odom_msg.pose.position.x = predict_pose_imu_odom.x;
    predict_pose_imu_odom_msg.pose.position.y = predict_pose_imu_odom.y;
    predict_pose_imu_odom_msg.pose.position.z = predict_pose_imu_odom.z;
    predict_pose_imu_odom_msg.pose.orientation.x = predict_q_imu_odom.x();
    predict_pose_imu_odom_msg.pose.orientation.y = predict_q_imu_odom.y();
    predict_pose_imu_odom_msg.pose.orientation.z = predict_q_imu_odom.z();
    predict_pose_imu_odom_msg.pose.orientation.w = predict_q_imu_odom.w();
    predict_pose_imu_odom_pub.publish(predict_pose_imu_odom_msg);

    //ndt_pose 计算出来的当前位置信息
    ndt_q.setRPY(ndt_pose.roll, ndt_pose.pitch, ndt_pose.yaw);
    if (_use_local_transform == true)
    {
      tf::Vector3 v(ndt_pose.x, ndt_pose.y, ndt_pose.z);
      tf::Transform transform(ndt_q, v);
      ndt_pose_msg.header.frame_id = "/map";
      ndt_pose_msg.header.stamp = current_scan_time;
      ndt_pose_msg.pose.position.x = (local_transform * transform).getOrigin().getX();
      ndt_pose_msg.pose.position.y = (local_transform * transform).getOrigin().getY();
      ndt_pose_msg.pose.position.z = (local_transform * transform).getOrigin().getZ();
      ndt_pose_msg.pose.orientation.x = (local_transform * transform).getRotation().x();
      ndt_pose_msg.pose.orientation.y = (local_transform * transform).getRotation().y();
      ndt_pose_msg.pose.orientation.z = (local_transform * transform).getRotation().z();
      ndt_pose_msg.pose.orientation.w = (local_transform * transform).getRotation().w();
    }
    else //默认值
    {
      ndt_pose_msg.header.frame_id = "/map";
      ndt_pose_msg.header.stamp = current_scan_time;
      ndt_pose_msg.pose.position.x = ndt_pose.x;
      ndt_pose_msg.pose.position.y = ndt_pose.y;
      ndt_pose_msg.pose.position.z = ndt_pose.z;
      ndt_pose_msg.pose.orientation.x = ndt_q.x();
      ndt_pose_msg.pose.orientation.y = ndt_q.y();
      ndt_pose_msg.pose.orientation.z = ndt_q.z();
      ndt_pose_msg.pose.orientation.w = ndt_q.w();
    }
    //current_pose 当前判断出来的位置,排除了异常匹配的更精准的位置
    current_q.setRPY(current_pose.roll, current_pose.pitch, current_pose.yaw);
    // current_pose is published by vel_pose_mux
    /*
    current_pose_msg.header.frame_id = "/map";
    current_pose_msg.header.stamp = current_scan_time;
    current_pose_msg.pose.position.x = current_pose.x;
    current_pose_msg.pose.position.y = current_pose.y;
    current_pose_msg.pose.position.z = current_pose.z;
    current_pose_msg.pose.orientation.x = current_q.x();
    current_pose_msg.pose.orientation.y = current_q.y();
    current_pose_msg.pose.orientation.z = current_q.z();
    current_pose_msg.pose.orientation.w = current_q.w();
    */
    //localizer_pose 当前雷达的位置
    localizer_q.setRPY(localizer_pose.roll, localizer_pose.pitch, localizer_pose.yaw);
    if (_use_local_transform == true)
    {
      tf::Vector3 v(localizer_pose.x, localizer_pose.y, localizer_pose.z);
      tf::Transform transform(localizer_q, v);
      localizer_pose_msg.header.frame_id = "/map";
      localizer_pose_msg.header.stamp = current_scan_time;
      localizer_pose_msg.pose.position.x = (local_transform * transform).getOrigin().getX();
      localizer_pose_msg.pose.position.y = (local_transform * transform).getOrigin().getY();
      localizer_pose_msg.pose.position.z = (local_transform * transform).getOrigin().getZ();
      localizer_pose_msg.pose.orientation.x = (local_transform * transform).getRotation().x();
      localizer_pose_msg.pose.orientation.y = (local_transform * transform).getRotation().y();
      localizer_pose_msg.pose.orientation.z = (local_transform * transform).getRotation().z();
      localizer_pose_msg.pose.orientation.w = (local_transform * transform).getRotation().w();
    }
    else
    {
      localizer_pose_msg.header.frame_id = "/map";
      localizer_pose_msg.header.stamp = current_scan_time;
      localizer_pose_msg.pose.position.x = localizer_pose.x;
      localizer_pose_msg.pose.position.y = localizer_pose.y;
      localizer_pose_msg.pose.position.z = localizer_pose.z;
      localizer_pose_msg.pose.orientation.x = localizer_q.x();
      localizer_pose_msg.pose.orientation.y = localizer_q.y();
      localizer_pose_msg.pose.orientation.z = localizer_q.z();
      localizer_pose_msg.pose.orientation.w = localizer_q.w();
    }

    predict_pose_pub.publish(predict_pose_msg);
    ndt_pose_pub.publish(ndt_pose_msg);
    // current_pose is published by vel_pose_mux
    //    current_pose_pub.publish(current_pose_msg);
    localizer_pose_pub.publish(localizer_pose_msg);

    // Send TF "/base_link" to "/map"
    // base_link到map原点的tf变换
    transform.setOrigin(tf::Vector3(current_pose.x, current_pose.y, current_pose.z));
    transform.setRotation(current_q);
    //    br.sendTransform(tf::StampedTransform(transform, current_scan_time, "/map", "/base_footprint"));
    if (_use_local_transform == true)
    {
      br.sendTransform(tf::StampedTransform(local_transform * transform, current_scan_time, "/map", "/base_footprint"));
    }
    else
    {
      //发步tf变换
      br.sendTransform(tf::StampedTransform(transform, current_scan_time, "/map", "/base_footprint"));
    }
    //计算匹配所需要的时间
    matching_end = std::chrono::system_clock::now();
    exe_time = std::chrono::duration_cast<std::chrono::microseconds>(matching_end - matching_start).count() / 1000.0;
    time_ndt_matching.data = exe_time;
    time_ndt_matching_pub.publish(time_ndt_matching);

    // Set values for /estimate_twist
    //发布底盘的运动速度
    estimate_twist_msg.header.stamp = current_scan_time;
    estimate_twist_msg.header.frame_id = "/base_link";
    estimate_twist_msg.twist.linear.x = current_velocity;
    estimate_twist_msg.twist.linear.y = 0.0;
    estimate_twist_msg.twist.linear.z = 0.0;
    estimate_twist_msg.twist.angular.x = 0.0;
    estimate_twist_msg.twist.angular.y = 0.0;
    estimate_twist_msg.twist.angular.z = angular_velocity;

    estimate_twist_pub.publish(estimate_twist_msg);

    geometry_msgs::Vector3Stamped estimate_vel_msg;
    estimate_vel_msg.header.stamp = current_scan_time;
    estimate_vel_msg.vector.x = current_velocity;
    estimated_vel_pub.publish(estimate_vel_msg);

    // Set values for /ndt_stat
    //发布这次匹配的中的数据值
    ndt_stat_msg.header.stamp = current_scan_time;//当前雷达的时间戳
    ndt_stat_msg.exe_time = time_ndt_matching.data;//此次匹配所花时间
    ndt_stat_msg.iteration = iteration;//迭代次数
    ndt_stat_msg.score = fitness_score;//匹配分数
    ndt_stat_msg.velocity = current_velocity;//当前速度
    ndt_stat_msg.acceleration = current_accel;//当前加速度
    ndt_stat_msg.use_predict_pose = 0;//匹配的数据与上次数据的差异值是否过大

    ndt_stat_pub.publish(ndt_stat_msg);
    /* Compute NDT_Reliability */
    //此次ndt匹配的可靠性
    ndt_reliability.data = Wa * (exe_time / 100.0) * 100.0 + Wb * (iteration / 10.0) * 100.0 +
                           Wc * ((2.0 - trans_probability) / 2.0) * 100.0;
    ndt_reliability_pub.publish(ndt_reliability);

    // Write log
    //ndt日志
    if(_output_log_data)
    {
      if (!ofs)
      {
        std::cerr << "Could not open " << filename << "." << std::endl;
      }
      else
      {
        ofs << input->header.seq << "," << scan_points_num << "," << step_size << "," << trans_eps << "," << std::fixed
            << std::setprecision(5) << current_pose.x << "," << std::fixed << std::setprecision(5) << current_pose.y << ","
            << std::fixed << std::setprecision(5) << current_pose.z << "," << current_pose.roll << "," << current_pose.pitch
            << "," << current_pose.yaw << "," << predict_pose.x << "," << predict_pose.y << "," << predict_pose.z << ","
            << predict_pose.roll << "," << predict_pose.pitch << "," << predict_pose.yaw << ","
            << current_pose.x - predict_pose.x << "," << current_pose.y - predict_pose.y << ","
            << current_pose.z - predict_pose.z << "," << current_pose.roll - predict_pose.roll << ","
            << current_pose.pitch - predict_pose.pitch << "," << current_pose.yaw - predict_pose.yaw << ","
            << predict_pose_error << "," << iteration << "," << fitness_score << "," << trans_probability << ","
            << ndt_reliability.data << "," << current_velocity << "," << current_velocity_smooth << "," << current_accel
            << "," << angular_velocity << "," << time_ndt_matching.data << "," << align_time << "," << getFitnessScore_time
            << std::endl;
      }
    }

    std::cout << "-----------------------------------------------------------------" << std::endl;
    std::cout << "Sequence: " << input->header.seq << std::endl;
    std::cout << "Timestamp: " << input->header.stamp << std::endl;
    std::cout << "Frame ID: " << input->header.frame_id << std::endl;
    //		std::cout << "Number of Scan Points: " << scan_ptr->size() << " points." << std::endl;
    std::cout << "Number of Filtered Scan Points: " << scan_points_num << " points." << std::endl;
    std::cout << "NDT has converged: " << has_converged << std::endl;
    std::cout << "Fitness Score: " << fitness_score << std::endl;
    std::cout << "Transformation Probability: " << trans_probability << std::endl;
    std::cout << "Execution Time: " << exe_time << " ms." << std::endl;
    std::cout << "Number of Iterations: " << iteration << std::endl;
    std::cout << "NDT Reliability: " << ndt_reliability.data << std::endl;
    std::cout << "(x,y,z,roll,pitch,yaw): " << std::endl;
    std::cout << "(" << current_pose.x << ", " << current_pose.y << ", " << current_pose.z << ", " << current_pose.roll
              << ", " << current_pose.pitch << ", " << current_pose.yaw << ")" << std::endl;
    std::cout << "Transformation Matrix: " << std::endl;
    std::cout << t << std::endl;
    std::cout << "Align time: " << align_time << std::endl;
    std::cout << "Get fitness score time: " << getFitnessScore_time << std::endl;
    std::cout << "-----------------------------------------------------------------" << std::endl;

    offset_imu_x = 0.0;
    offset_imu_y = 0.0;
    offset_imu_z = 0.0;
    offset_imu_roll = 0.0;
    offset_imu_pitch = 0.0;
    offset_imu_yaw = 0.0;

    offset_odom_x = 0.0;
    offset_odom_y = 0.0;
    offset_odom_z = 0.0;
    offset_odom_roll = 0.0;
    offset_odom_pitch = 0.0;
    offset_odom_yaw = 0.0;

    offset_imu_odom_x = 0.0;
    offset_imu_odom_y = 0.0;
    offset_imu_odom_z = 0.0;
    offset_imu_odom_roll = 0.0;
    offset_imu_odom_pitch = 0.0;
    offset_imu_odom_yaw = 0.0;

    // Update previous_***
    //更新上一时刻数据值
    previous_pose.x = current_pose.x;
    previous_pose.y = current_pose.y;
    previous_pose.z = current_pose.z;
    previous_pose.roll = current_pose.roll;
    previous_pose.pitch = current_pose.pitch;
    previous_pose.yaw = current_pose.yaw;

    previous_scan_time = current_scan_time;

    previous_previous_velocity = previous_velocity;
    previous_velocity = current_velocity;
    previous_velocity_x = current_velocity_x;
    previous_velocity_y = current_velocity_y;
    previous_velocity_z = current_velocity_z;
    previous_accel = current_accel;

    previous_estimated_vel_kmph.data = estimated_vel_kmph.data;
  }
}

 

;