Cartographer（10）CeresScanMatcher2D

  

Ceres Solver：从入门到使用_非晚非晚的博客-CSDN博客_ceres核函数

基本原理公式： 

LocalTrajectoryBuilder2D::ScanMatch --》 ceres_scan_matcher_.Match

/**
 * @brief 基于Ceres的扫描匹配
 * 
 * @param[in] target_translation 预测出来的先验位置, 只有xy
 * @param[in] initial_pose_estimate (校正后的)先验位姿, 有xy与theta
 * @param[in] point_cloud 用于匹配的点云 点云的原点位于local坐标系原点
 * @param[in] grid 用于匹配的栅格地图
 * @param[out] pose_estimate 优化之后的位姿
 * @param[out] summary 
 */
void CeresScanMatcher2D::Match(const Eigen::Vector2d& target_translation,
                               const transform::Rigid2d& initial_pose_estimate,
                               const sensor::PointCloud& point_cloud,
                               const Grid2D& grid,
                               transform::Rigid2d* const pose_estimate,
                               ceres::Solver::Summary* const summary) const {
  double ceres_pose_estimate[3] = {initial_pose_estimate.translation().x(),
                                   initial_pose_estimate.translation().y(),
                                   initial_pose_estimate.rotation().angle()};
  ceres::Problem problem;

  // 地图部分的残差
  CHECK_GT(options_.occupied_space_weight(), 0.);
  switch (grid.GetGridType()) {
    case GridType::PROBABILITY_GRID:  //使用的使概率栅格地图
      problem.AddResidualBlock(
          CreateOccupiedSpaceCostFunction2D(
              options_.occupied_space_weight() /
                  std::sqrt(static_cast(point_cloud.size())),
              point_cloud, grid),
          nullptr /* loss function */, ceres_pose_estimate);
      break;
    case GridType::TSDF:
      problem.AddResidualBlock(
          CreateTSDFMatchCostFunction2D(
              options_.occupied_space_weight() /
                  std::sqrt(static_cast(point_cloud.size())),
              point_cloud, static_cast(grid)),
          nullptr /* loss function */, ceres_pose_estimate);
      break;
  }

  // 平移的残差
  CHECK_GT(options_.translation_weight(), 0.);
  problem.AddResidualBlock(
      TranslationDeltaCostFunctor2D::CreateAutoDiffCostFunction(
          options_.translation_weight(), target_translation), // 平移的目标值, 没有使用校准后的平移
      nullptr /* loss function */, ceres_pose_estimate);      // 平移的初值

  // 旋转的残差, 固定了角度不变
  CHECK_GT(options_.rotation_weight(), 0.);
  problem.AddResidualBlock(
      RotationDeltaCostFunctor2D::CreateAutoDiffCostFunction(
          options_.rotation_weight(), ceres_pose_estimate[2]), // 角度的目标值
      nullptr /* loss function */, ceres_pose_estimate);       // 角度的初值

  // 根据配置进行求解
  ceres::Solve(ceres_solver_options_, &problem, summary);

  *pose_estimate = transform::Rigid2d(
      {ceres_pose_estimate[0], ceres_pose_estimate[1]}, ceres_pose_estimate[2]);
}

CreateOccupiedSpaceCostFunction2D 

// 工厂函数, 返回地图的CostFunction
ceres::CostFunction* CreateOccupiedSpaceCostFunction2D(
    const double scaling_factor, const sensor::PointCloud& point_cloud,
    const Grid2D& grid) {
  return new ceres::AutoDiffCostFunction(
      new OccupiedSpaceCostFunction2D(scaling_factor, point_cloud, grid),
      point_cloud.size()); // 比固定残差维度的 多了一个参数
}

 操作函数

 bool operator()(const T* const pose, T* residual) const {
    Eigen::Matrix translation(pose[0], pose[1]);
    Eigen::Rotation2D rotation(pose[2]);
    Eigen::Matrix rotation_matrix = rotation.toRotationMatrix();
    Eigen::Matrix transform;
    transform << rotation_matrix, translation, T(0.), T(0.), T(1.);

    const GridArrayAdapter adapter(grid_);
    ceres::BiCubicInterpolator interpolator(adapter);
    const MapLimits& limits = grid_.limits();

    for (size_t i = 0; i < point_cloud_.size(); ++i) {
      // Note that this is a 2D point. The third component is a scaling factor.
      const Eigen::Matrix point((T(point_cloud_[i].position.x())),
                                         (T(point_cloud_[i].position.y())),
                                         T(1.));
      // 根据预测位姿对单个点进行坐标变换
      const Eigen::Matrix world = transform * point;
      // 获取三次插值之后的栅格free值, Evaluate函数内部调用了GetValue函数
      interpolator.Evaluate(
          (limits.max().x() - world[0]) / limits.resolution() - 0.5 +
              static_cast(kPadding),
          (limits.max().y() - world[1]) / limits.resolution() - 0.5 +
              static_cast(kPadding),
          &residual[i]);
      // free值越小, 表示占用的概率越大
      residual[i] = scaling_factor_ * residual[i];
    }
    return true;
  }

 

 平移矩阵：   

 

旋转矩阵：

 

旋转平移矩阵：

 const Eigen::Matrix world = transform * point;