cartographer的后端核心,后端优化OptimizationProblem2D::Solve

简介:cartograoher使用位姿图优化来slam,从代码上来看是一股脑的处理所有数据,这样当数据不断增长时就很不利,可以试试因子图的方案,或者是vins的策略。cartographer也不是一点也没有,他使用了frozen_trajectories的策略,就是该路径不需要再优化就设置为frozen,但是何时设置为Frozen呢?这是个问题,所以cartographer是希望大地图是由小地图拼起来的,每次建小地图然后保存,然后再加载时设置为frozen,然后再建新图再一起保存。
虽然功能上还是有些可以优化的地方,但是这段代码写的真优雅。

talk is cheap, show other’s code

void OptimizationProblem2D::Solve(
    const std::vector<Constraint>& constraints,
    const std::map<int, PoseGraphInterface::TrajectoryState>&
        trajectories_state,
    const std::map<std::string, LandmarkNode>& landmark_nodes) {
  if (node_data_.empty()) {
    // Nothing to optimize.
    return;
  }
  //
  std::set<int> frozen_trajectories;
  std::set<int> idle_trajectories;
  for (const auto& it : trajectories_state) {
    if (it.second == PoseGraphInterface::TrajectoryState::FROZEN) {
      frozen_trajectories.insert(it.first);
    }
    else if(it.second == PoseGraphInterface::TrajectoryState::IDLE){
      idle_trajectories.insert(it.first);
    }
  }

  ceres::Problem::Options problem_options;
  ceres::Problem problem(problem_options);

  // Set the starting point.
  // TODO(hrapp): Move ceres data into SubmapSpec.
  MapById<SubmapId, std::array<double, 3>> C_submaps;
  MapById<NodeId, std::array<double, 3>> C_nodes;
  std::map<std::string, CeresPose> C_landmarks;
  bool first_submap = true;
  //遍历每个活跃的子图
  for (const auto& submap_id_data : submap_data_) {
    //okagv
    //查询该子图所在的路径是否需要优化,这个是博主自己添加的
    const bool idle = 
         idle_trajectories.count(submap_id_data.id.trajectory_id) != 0;
    if(idle) continue;
    //查询该子图所在的路径是否是个固定位姿。
    const bool frozen =
        frozen_trajectories.count(submap_id_data.id.trajectory_id) != 0;
    C_submaps.Insert(submap_id_data.id,
                     FromPose(submap_id_data.data.global_pose));
    //把要优化的位姿添加进去
    problem.AddParameterBlock(C_submaps.at(submap_id_data.id).data(), 3);
    if (first_submap || frozen) {
      first_submap = false;
      // Fix the pose of the first submap or all submaps of a frozen
      // trajectory.
      //如果是第一张子图或者该子图是frozen,则不优化并设置为常数。
      problem.SetParameterBlockConstant(C_submaps.at(submap_id_data.id).data());
    }
  }

  //遍历每一个活跃的路径节点
  for (const auto& node_id_data : node_data_) {
     //同上
    const bool idle = 
         idle_trajectories.count(node_id_data.id.trajectory_id) != 0;
    if(idle) continue;
    //同上
    const bool frozen =
        frozen_trajectories.count(node_id_data.id.trajectory_id) != 0;
    C_nodes.Insert(node_id_data.id, FromPose(node_id_data.data.global_pose_2d));
    //把要优化的路径节点添加进去
    problem.AddParameterBlock(C_nodes.at(node_id_data.id).data(), 3);
    //同上
    if (frozen) {
      problem.SetParameterBlockConstant(C_nodes.at(node_id_data.id).data());
    }
  }
  // Add cost functions for intra- and inter-submap constraints.
  //添加约束变量构建残差方程
  for (const Constraint& constraint : constraints) {
      //同上
      bool idle =
              ((idle_trajectories.count(constraint.submap_id.trajectory_id) != 0) ||
              (idle_trajectories.count(constraint.node_id.trajectory_id) != 0));
     if(idle) continue;
    //构建子图位姿与路径位姿的残差方程,ceres传入数据是指针的方式,所以最后优化结果保存在C_submaps和C_nodes里
    problem.AddResidualBlock(
        CreateAutoDiffSpaCostFunction(constraint.pose),
        // Loop closure constraints should have a loss function.
        constraint.tag == Constraint::INTER_SUBMAP
            ? new ceres::HuberLoss(options_.huber_scale())
            : nullptr,
        C_submaps.at(constraint.submap_id).data(),
        C_nodes.at(constraint.node_id).data());
  }
  // Add cost functions for landmarks.
  //LOG(INFO) << "Peak.ding landmark_nodes.size " << landmark_nodes.size();
  //构建Landmark与路径节点之间的位姿
  AddLandmarkCostFunctions(landmark_nodes, node_data_, &C_nodes, &C_landmarks,
                           &problem, options_.huber_scale(), trajectories_state);
  // Add penalties for violating odometry or changes between consecutive nodes
  // if odometry is not available.
  //如果有里程计数据(一般指齿轮编码器的数据)和slam pose,则构建路径节点之间的约束,防止优化后的路径失去连续性
  for (auto node_it = node_data_.begin(); node_it != node_data_.end();) {
    const int trajectory_id = node_it->id.trajectory_id;
    const auto trajectory_end = node_data_.EndOfTrajectory(trajectory_id);
    if (frozen_trajectories.count(trajectory_id) != 0) {
      node_it = trajectory_end;
      continue;
    }

    if (idle_trajectories.count(trajectory_id) != 0){
      node_it = trajectory_end;
      continue;
    }

    auto prev_node_it = node_it;
    for (++node_it; node_it != trajectory_end; ++node_it) {
      const NodeId first_node_id = prev_node_it->id;
      const NodeSpec2D& first_node_data = prev_node_it->data;
      prev_node_it = node_it;
      const NodeId second_node_id = node_it->id;
      const NodeSpec2D& second_node_data = node_it->data;

      if (second_node_id.node_index != first_node_id.node_index + 1) {
        continue;
      }

      // Add a relative pose constraint based on the odometry (if available).
      //如果有齿轮编码器数据
      std::unique_ptr<transform::Rigid3d> relative_odometry =
          CalculateOdometryBetweenNodes(trajectory_id, first_node_data,
                                        second_node_data);
      if (relative_odometry != nullptr) {
        problem.AddResidualBlock(
            CreateAutoDiffSpaCostFunction(Constraint::Pose{
                *relative_odometry, options_.odometry_translation_weight(),
                options_.odometry_rotation_weight()}),
            nullptr /* loss function */, C_nodes.at(first_node_id).data(),
            C_nodes.at(second_node_id).data());
      }

      // Add a relative pose constraint based on consecutive local SLAM poses.
      //使用局部的local SLAM pose
      const transform::Rigid3d relative_local_slam_pose =
          transform::Embed3D(first_node_data.local_pose_2d.inverse() *
                             second_node_data.local_pose_2d);
      //添加约束
      problem.AddResidualBlock(
          CreateAutoDiffSpaCostFunction(
              Constraint::Pose{relative_local_slam_pose,
                               options_.local_slam_pose_translation_weight(),
                               options_.local_slam_pose_rotation_weight()}),
          nullptr /* loss function */, C_nodes.at(first_node_id).data(),
          C_nodes.at(second_node_id).data());
    }
  }
  //fixed_frame一般指GPS数据,这里我们没有用到
  std::map<int, std::array<double, 3>> C_fixed_frames;
  for (auto node_it = node_data_.begin(); node_it != node_data_.end();) {
    const int trajectory_id = node_it->id.trajectory_id;
    const auto trajectory_end = node_data_.EndOfTrajectory(trajectory_id);
    if (!fixed_frame_pose_data_.HasTrajectory(trajectory_id)) {
      node_it = trajectory_end;
      continue;
    }

    const TrajectoryData& trajectory_data = trajectory_data_.at(trajectory_id);
    bool fixed_frame_pose_initialized = false;
    for (; node_it != trajectory_end; ++node_it) {
      const NodeId node_id = node_it->id;
      const NodeSpec2D& node_data = node_it->data;

      const std::unique_ptr<transform::Rigid3d> fixed_frame_pose =
          Interpolate(fixed_frame_pose_data_, trajectory_id, node_data.time);
      if (fixed_frame_pose == nullptr) {
        continue;
      }

      const Constraint::Pose constraint_pose{
          *fixed_frame_pose, options_.fixed_frame_pose_translation_weight(),
          options_.fixed_frame_pose_rotation_weight()};

      if (!fixed_frame_pose_initialized) {
        transform::Rigid2d fixed_frame_pose_in_map;
        if (trajectory_data.fixed_frame_origin_in_map.has_value()) {
          fixed_frame_pose_in_map = transform::Project2D(
              trajectory_data.fixed_frame_origin_in_map.value());
        } else {
          fixed_frame_pose_in_map =
              node_data.global_pose_2d *
              transform::Project2D(constraint_pose.zbar_ij).inverse();
        }

        C_fixed_frames.emplace(trajectory_id,
                               FromPose(fixed_frame_pose_in_map));
        fixed_frame_pose_initialized = true;
      }

      problem.AddResidualBlock(
          CreateAutoDiffSpaCostFunction(constraint_pose),
          options_.fixed_frame_pose_use_tolerant_loss()
              ? new ceres::TolerantLoss(
                    options_.fixed_frame_pose_tolerant_loss_param_a(),
                    options_.fixed_frame_pose_tolerant_loss_param_b())
              : nullptr,
          C_fixed_frames.at(trajectory_id).data(), C_nodes.at(node_id).data());
    }
  }
  
  // Solve.
  //方程非线性优化开始
  ceres::Solver::Summary summary;
  ceres::Solve(
      common::CreateCeresSolverOptions(options_.ceres_solver_options()),
      &problem, &summary);
  if (options_.log_solver_summary()) {
    LOG(INFO) << summary.FullReport();
  }

  // Store the result.
  //结果都保存在C_submaps、C_nodes、C_fixed_frames、C_landmarks里
  for (const auto& C_submap_id_data : C_submaps) {
    submap_data_.at(C_submap_id_data.id).global_pose =
        ToPose(C_submap_id_data.data);
  }
  for (const auto& C_node_id_data : C_nodes) {
    node_data_.at(C_node_id_data.id).global_pose_2d =
        ToPose(C_node_id_data.data);
  }
  for (const auto& C_fixed_frame : C_fixed_frames) {
    trajectory_data_.at(C_fixed_frame.first).fixed_frame_origin_in_map =
        transform::Embed3D(ToPose(C_fixed_frame.second));
  }
  for (const auto& C_landmark : C_landmarks) {
    landmark_data_[C_landmark.first] = C_landmark.second.ToRigid();
  }
  //LOG(INFO) << "Peak.ding landmark_data_ size " << landmark_data_.size();
}
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