首先回顾一下前面的分析，我们知道main函数最主要的工作是调用了Run这个函数，在前面的篇章中，我们学习到了LoadOptions函数并且也学了CreateMapBuilder这个函数，那么继续往下读程序，就是到了创建浓node类：

 Node node(node_options, std::move(map_builder), &tf_buffer,
            FLAGS_collect_metrics);

因此我们接下来就要好好分析 Node类的构造与实现

class Node {
 public:
  Node(const NodeOptions& node_options,
       std::unique_ptr<cartographer::mapping::MapBuilderInterface> map_builder,
       tf2_ros::Buffer* tf_buffer, bool collect_metrics);
  ~Node();

  Node(const Node&) = delete;
  Node& operator=(const Node&) = delete;

  // Finishes all yet active trajectories.
  void FinishAllTrajectories();
  // Finishes a single given trajectory. Returns false if the trajectory did not
  // exist or was already finished.
  bool FinishTrajectory(int trajectory_id);

  // Runs final optimization. All trajectories have to be finished when calling.
  void RunFinalOptimization();

  // Starts the first trajectory with the default topics.
  void StartTrajectoryWithDefaultTopics(const TrajectoryOptions& options);

  // Returns unique SensorIds for multiple input bag files based on
  // their TrajectoryOptions.
  // 'SensorId::id' is the expected ROS topic name.
  std::vector<
      std::set<::cartographer::mapping::TrajectoryBuilderInterface::SensorId>>
  ComputeDefaultSensorIdsForMultipleBags(
      const std::vector<TrajectoryOptions>& bags_options) const;

  // Adds a trajectory for offline processing, i.e. not listening to topics.
  int AddOfflineTrajectory(
      const std::set<
          cartographer::mapping::TrajectoryBuilderInterface::SensorId>&
          expected_sensor_ids,
      const TrajectoryOptions& options);

  // The following functions handle adding sensor data to a trajectory.
  void HandleOdometryMessage(int trajectory_id, const std::string& sensor_id,
                             const nav_msgs::Odometry::ConstPtr& msg);
  void HandleNavSatFixMessage(int trajectory_id, const std::string& sensor_id,
                              const sensor_msgs::NavSatFix::ConstPtr& msg);
  void HandleLandmarkMessage(
      int trajectory_id, const std::string& sensor_id,
      const cartographer_ros_msgs::LandmarkList::ConstPtr& msg);
  void HandleImuMessage(int trajectory_id, const std::string& sensor_id,
                        const sensor_msgs::Imu::ConstPtr& msg);
  void HandleLaserScanMessage(int trajectory_id, const std::string& sensor_id,
                              const sensor_msgs::LaserScan::ConstPtr& msg);
  void HandleMultiEchoLaserScanMessage(
      int trajectory_id, const std::string& sensor_id,
      const sensor_msgs::MultiEchoLaserScan::ConstPtr& msg);
  void HandlePointCloud2Message(int trajectory_id, const std::string& sensor_id,
                                const sensor_msgs::PointCloud2::ConstPtr& msg);

  // Serializes the complete Node state.
  void SerializeState(const std::string& filename,
                      const bool include_unfinished_submaps);

  // Loads a serialized SLAM state from a .pbstream file.
  void LoadState(const std::string& state_filename, bool load_frozen_state);

  ::ros::NodeHandle* node_handle();

 private:
  struct Subscriber {
    ::ros::Subscriber subscriber;

    // ::ros::Subscriber::getTopic() does not necessarily return the same
    // std::string
    // it was given in its constructor. Since we rely on the topic name as the
    // unique identifier of a subscriber, we remember it ourselves.
    std::string topic;
  };

  bool HandleSubmapQuery(
      cartographer_ros_msgs::SubmapQuery::Request& request,
      cartographer_ros_msgs::SubmapQuery::Response& response);
  bool HandleTrajectoryQuery(
      ::cartographer_ros_msgs::TrajectoryQuery::Request& request,
      ::cartographer_ros_msgs::TrajectoryQuery::Response& response);
  bool HandleStartTrajectory(
      cartographer_ros_msgs::StartTrajectory::Request& request,
      cartographer_ros_msgs::StartTrajectory::Response& response);
  bool HandleFinishTrajectory(
      cartographer_ros_msgs::FinishTrajectory::Request& request,
      cartographer_ros_msgs::FinishTrajectory::Response& response);
  bool HandleWriteState(cartographer_ros_msgs::WriteState::Request& request,
                        cartographer_ros_msgs::WriteState::Response& response);
  bool HandleGetTrajectoryStates(
      ::cartographer_ros_msgs::GetTrajectoryStates::Request& request,
      ::cartographer_ros_msgs::GetTrajectoryStates::Response& response);
  bool HandleReadMetrics(
      cartographer_ros_msgs::ReadMetrics::Request& request,
      cartographer_ros_msgs::ReadMetrics::Response& response);

  // Returns the set of SensorIds expected for a trajectory.
  // 'SensorId::id' is the expected ROS topic name.
  std::set<::cartographer::mapping::TrajectoryBuilderInterface::SensorId>
  ComputeExpectedSensorIds(const TrajectoryOptions& options) const;
  int AddTrajectory(const TrajectoryOptions& options);
  void LaunchSubscribers(const TrajectoryOptions& options, int trajectory_id);
  void PublishSubmapList(const ::ros::WallTimerEvent& timer_event);
  void AddExtrapolator(int trajectory_id, const TrajectoryOptions& options);
  void AddSensorSamplers(int trajectory_id, const TrajectoryOptions& options);
  void PublishLocalTrajectoryData(const ::ros::TimerEvent& timer_event);
  void PublishTrajectoryNodeList(const ::ros::WallTimerEvent& timer_event);
  void PublishLandmarkPosesList(const ::ros::WallTimerEvent& timer_event);
  void PublishConstraintList(const ::ros::WallTimerEvent& timer_event);
  bool ValidateTrajectoryOptions(const TrajectoryOptions& options);
  bool ValidateTopicNames(const TrajectoryOptions& options);
  cartographer_ros_msgs::StatusResponse FinishTrajectoryUnderLock(
      int trajectory_id) EXCLUSIVE_LOCKS_REQUIRED(mutex_);
  void MaybeWarnAboutTopicMismatch(const ::ros::WallTimerEvent&);

  // Helper function for service handlers that need to check trajectory states.
  cartographer_ros_msgs::StatusResponse TrajectoryStateToStatus(
      int trajectory_id,
      const std::set<
          cartographer::mapping::PoseGraphInterface::TrajectoryState>&
          valid_states);
  const NodeOptions node_options_;

  tf2_ros::TransformBroadcaster tf_broadcaster_;

  absl::Mutex mutex_;
  std::unique_ptr<cartographer_ros::metrics::FamilyFactory> metrics_registry_;
  MapBuilderBridge map_builder_bridge_ GUARDED_BY(mutex_);

  ::ros::NodeHandle node_handle_;
  ::ros::Publisher submap_list_publisher_;
  ::ros::Publisher trajectory_node_list_publisher_;
  ::ros::Publisher landmark_poses_list_publisher_;
  ::ros::Publisher constraint_list_publisher_;
  ::ros::Publisher tracked_pose_publisher_;
  // These ros::ServiceServers need to live for the lifetime of the node.
  std::vector<::ros::ServiceServer> service_servers_;
  ::ros::Publisher scan_matched_point_cloud_publisher_;

  struct TrajectorySensorSamplers {
    TrajectorySensorSamplers(const double rangefinder_sampling_ratio,
                             const double odometry_sampling_ratio,
                             const double fixed_frame_pose_sampling_ratio,
                             const double imu_sampling_ratio,
                             const double landmark_sampling_ratio)
        : rangefinder_sampler(rangefinder_sampling_ratio),
          odometry_sampler(odometry_sampling_ratio),
          fixed_frame_pose_sampler(fixed_frame_pose_sampling_ratio),
          imu_sampler(imu_sampling_ratio),
          landmark_sampler(landmark_sampling_ratio) {}

    ::cartographer::common::FixedRatioSampler rangefinder_sampler;
    ::cartographer::common::FixedRatioSampler odometry_sampler;
    ::cartographer::common::FixedRatioSampler fixed_frame_pose_sampler;
    ::cartographer::common::FixedRatioSampler imu_sampler;
    ::cartographer::common::FixedRatioSampler landmark_sampler;
  };

  // These are keyed with 'trajectory_id'.
  std::map<int, ::cartographer::mapping::PoseExtrapolator> extrapolators_;
  std::map<int, ::ros::Time> last_published_tf_stamps_;
  std::unordered_map<int, TrajectorySensorSamplers> sensor_samplers_;
  std::unordered_map<int, std::vector<Subscriber>> subscribers_;
  std::unordered_set<std::string> subscribed_topics_;
  std::unordered_set<int> trajectories_scheduled_for_finish_;

  // We have to keep the timer handles of ::ros::WallTimers around, otherwise
  // they do not fire.
  std::vector<::ros::WallTimer> wall_timers_;

  // The timer for publishing local trajectory data (i.e. pose transforms and
  // range data point clouds) is a regular timer which is not triggered when
  // simulation time is standing still. This prevents overflowing the transform
  // listener buffer by publishing the same transforms over and over again.
  ::ros::Timer publish_local_trajectory_data_timer_;
};

点进去我们发现，这个类特别庞大，这仅仅是类的声明，还没有跳转到类的实现。但是不要怕，我们接下来逐行分析。

  Node(const Node&) = delete;
  Node& operator=(const Node&) = delete;

当将复制构造函数和赋值运算符声明为 = delete 时，意味着我们禁止了通过复制构造函数和赋值运算符创建对象的副本或进行对象的赋值操作。
不懂的可以看下下面的例子：

class MyClass {
public:
  MyClass() { ... }

  // 禁用复制构造函数
  MyClass(const MyClass&) = delete;

  // 禁用赋值运算符
  MyClass& operator=(const MyClass&) = delete;
};

int main() {
  MyClass obj1;
  
  // 使用复制构造函数创建对象的副本（错误，已被禁用）
  MyClass obj2 = obj1;  // 编译错误

  // 使用赋值运算符进行对象的赋值操作（错误，已被禁用）
  MyClass obj3;
  obj3 = obj1;  // 编译错误

  return 0;
}

  std::vector<
      std::set<::cartographer::mapping::TrajectoryBuilderInterface::SensorId>>
  ComputeDefaultSensorIdsForMultipleBags(
      const std::vector<TrajectoryOptions>& bags_options) const;

返回类型：std::vector<std::set<::cartographer::mapping::TrajectoryBuilderInterface::SensorId>>
函数名称：ComputeDefaultSensorIdsForMultipleBags
参数：const std::vector& bags_options，一个包含多个数据包选项的向量。
函数的功能是根据每个数据包的选项，计算并返回一个包含多个数据包的默认传感器ID的集合。
我们还是对类中的成员的每个函数的实现具体分析一下吧。
我们先从开始轨迹开始分析：

  void StartTrajectoryWithDefaultTopics(const TrajectoryOptions& options);

函数实现：

void Node::StartTrajectoryWithDefaultTopics(const TrajectoryOptions& options) {
  absl::MutexLock lock(&mutex_);
  CHECK(ValidateTrajectoryOptions(options));
  AddTrajectory(options);
}

我们先看它调用的第一个函数：ValidateTrajectoryOptions，检验配置文件使用的轨迹构建的方式是2D还是3D

/*
  用于验证传入的轨迹选项“Options”是否与节点的配置相匹配，
  节点配置中有两种可能的轨迹选项：2D构建器和3D构建器
*/
bool Node::ValidateTrajectoryOptions(const TrajectoryOptions& options) {
  // 检查节点配置中是否选择了使用2D构建器
  if (node_options_.map_builder_options.use_trajectory_builder_2d()) {
    return options.trajectory_builder_options
        .has_trajectory_builder_2d_options();
  }
  // 检查节点配置中是否选择了使用3D构建器
  if (node_options_.map_builder_options.use_trajectory_builder_3d()) {
    return options.trajectory_builder_options
        .has_trajectory_builder_3d_options();
  }
  return false;
}

接下来我们再来看看第二个被调用的函数：

AddTrajectory(options);

然后看看它的内部实现

int Node::AddTrajectory(const TrajectoryOptions& options) {
  const std::set<cartographer::mapping::TrajectoryBuilderInterface::SensorId>
      expected_sensor_ids = ComputeExpectedSensorIds(options);
  const int trajectory_id =
      map_builder_bridge_.AddTrajectory(expected_sensor_ids, options);
  AddExtrapolator(trajectory_id, options);
  AddSensorSamplers(trajectory_id, options);
  LaunchSubscribers(options, trajectory_id);
  wall_timers_.push_back(node_handle_.createWallTimer(
      ::ros::WallDuration(kTopicMismatchCheckDelaySec),
      &Node::MaybeWarnAboutTopicMismatch, this, /*oneshot=*/true));
  for (const auto& sensor_id : expected_sensor_ids) {
    subscribed_topics_.insert(sensor_id.id);
  }
  return trajectory_id;
}

这个函数内部其中又调用了不少函数，我们一个一个看：

  const std::set<cartographer::mapping::TrajectoryBuilderInterface::SensorId>
      expected_sensor_ids = ComputeExpectedSensorIds(options);

函数实现：

std::set<cartographer::mapping::TrajectoryBuilderInterface::SensorId>
Node::ComputeExpectedSensorIds(const TrajectoryOptions& options) const {
  using SensorId = cartographer::mapping::TrajectoryBuilderInterface::SensorId;
  using SensorType = SensorId::SensorType;
  std::set<SensorId> expected_topics;
  // Subscribe to all laser scan, multi echo laser scan, and point cloud topics.
  for (const std::string& topic :
       ComputeRepeatedTopicNames(kLaserScanTopic, options.num_laser_scans)) {
    expected_topics.insert(SensorId{SensorType::RANGE, topic});
  }
  for (const std::string& topic : ComputeRepeatedTopicNames(
           kMultiEchoLaserScanTopic, options.num_multi_echo_laser_scans)) {
    expected_topics.insert(SensorId{SensorType::RANGE, topic});
  }
  for (const std::string& topic :
       ComputeRepeatedTopicNames(kPointCloud2Topic, options.num_point_clouds)) {
    expected_topics.insert(SensorId{SensorType::RANGE, topic});
  }
  // For 2D SLAM, subscribe to the IMU if we expect it. For 3D SLAM, the IMU is
  // required.
  if (node_options_.map_builder_options.use_trajectory_builder_3d() ||
      (node_options_.map_builder_options.use_trajectory_builder_2d() &&
       options.trajectory_builder_options.trajectory_builder_2d_options()
           .use_imu_data())) {
    expected_topics.insert(SensorId{SensorType::IMU, kImuTopic});
  }
  // Odometry is optional.
  if (options.use_odometry) {
    expected_topics.insert(SensorId{SensorType::ODOMETRY, kOdometryTopic});
  }
  // NavSatFix is optional.
  if (options.use_nav_sat) {
    expected_topics.insert(
        SensorId{SensorType::FIXED_FRAME_POSE, kNavSatFixTopic});
  }
  // Landmark is optional.
  if (options.use_landmarks) {
    expected_topics.insert(SensorId{SensorType::LANDMARK, kLandmarkTopic});
  }
  return expected_topics;
}

可以看到这个函数主要调用的是ComputeRepeatedTopicNames
函数实现，注释已经加上了

std::vector<std::string> ComputeRepeatedTopicNames(const std::string& topic,
                                                   const int num_topics) {
  //  检查num_topics的值是否大于等于0 
  CHECK_GE(num_topics, 0);
  // 如果是1，表示只有一个话题，直接将该话题返回
  if (num_topics == 1) {
    return {topic};
  }
  std::vector<std::string> topics;
  topics.reserve(num_topics);
  // 如果大于1，使用for循环，从0到num_topics-1遍历，每个话题又topic+_+num组成
  for (int i = 0; i < num_topics; ++i) {
    // 使用emplace_back函数将拼接而成的字符串添加到topics向量末尾，
    // emplace_back函数可以直接在容器中创造新的元素，无需进行额外的拷贝与移动操作。
    topics.emplace_back(topic + "_" + std::to_string(i + 1));
  }
  return topics;
}

简而言之，ComputeRepeatedTopicNames 函数根据给定的话题名称和数量生成一系列重复的话题名称，并将这些话题名称存储在字符串向量中返回。如果只有一个话题，直接返回包含该话题的字符串向量。其他传感器类似。
继续看ComputeExpectedSensorIds这个函数

 // For 2D SLAM, subscribe to the IMU if we expect it. For 3D SLAM, the IMU is
  // required.
  if (node_options_.map_builder_options.use_trajectory_builder_3d() ||
      (node_options_.map_builder_options.use_trajectory_builder_2d() &&
       options.trajectory_builder_options.trajectory_builder_2d_options()
           .use_imu_data())) {
    expected_topics.insert(SensorId{SensorType::IMU, kImuTopic});
  }

这个就是看使用2D还是3D了
函数最后将会返回： std::set expected_topics;也就是传感器的ID。
接下来看下一个函数AddTrajectory，咋一看这好像是再自己调用自己，但其实是两个作用域不同的函数

map_builder_bridge_.AddTrajectory(expected_sensor_ids, options);

函数实现

int MapBuilderBridge::AddTrajectory(
    const std::set<cartographer::mapping::TrajectoryBuilderInterface::SensorId>&
        expected_sensor_ids,
    const TrajectoryOptions& trajectory_options) {
  const int trajectory_id = map_builder_->AddTrajectoryBuilder(
      expected_sensor_ids, trajectory_options.trajectory_builder_options,
      [this](const int trajectory_id, const ::cartographer::common::Time time,
             const Rigid3d local_pose,
             ::cartographer::sensor::RangeData range_data_in_local,
             const std::unique_ptr<
                 const ::cartographer::mapping::TrajectoryBuilderInterface::
                     InsertionResult>) {
        OnLocalSlamResult(trajectory_id, time, local_pose, range_data_in_local);
      });
  LOG(INFO) << "Added trajectory with ID '" << trajectory_id << "'.";

  // Make sure there is no trajectory with 'trajectory_id' yet.
  CHECK_EQ(sensor_bridges_.count(trajectory_id), 0);
  sensor_bridges_[trajectory_id] = absl::make_unique<SensorBridge>(
      trajectory_options.num_subdivisions_per_laser_scan,
      trajectory_options.ignore_out_of_order_messages,
      trajectory_options.tracking_frame,
      node_options_.lookup_transform_timeout_sec, tf_buffer_,
      map_builder_->GetTrajectoryBuilder(trajectory_id));
  auto emplace_result =
      trajectory_options_.emplace(trajectory_id, trajectory_options);
  CHECK(emplace_result.second == true);
  return trajectory_id;
}

其中首先调用了：

 const int trajectory_id = map_builder_->AddTrajectoryBuilder

int MapBuilder::AddTrajectoryBuilder(
    const std::set<SensorId>& expected_sensor_ids,
    const proto::TrajectoryBuilderOptions& trajectory_options,
    LocalSlamResultCallback local_slam_result_callback) {
  const int trajectory_id = trajectory_builders_.size();

  absl::optional<MotionFilter> pose_graph_odometry_motion_filter;
  if (trajectory_options.has_pose_graph_odometry_motion_filter()) {
    LOG(INFO) << "Using a motion filter for adding odometry to the pose graph.";
    pose_graph_odometry_motion_filter.emplace(
        MotionFilter(trajectory_options.pose_graph_odometry_motion_filter()));
  }

  if (options_.use_trajectory_builder_3d()) {
    std::unique_ptr<LocalTrajectoryBuilder3D> local_trajectory_builder;
    if (trajectory_options.has_trajectory_builder_3d_options()) {
      local_trajectory_builder = absl::make_unique<LocalTrajectoryBuilder3D>(
          trajectory_options.trajectory_builder_3d_options(),
          SelectRangeSensorIds(expected_sensor_ids));
    }
    DCHECK(dynamic_cast<PoseGraph3D*>(pose_graph_.get()));
    trajectory_builders_.push_back(absl::make_unique<CollatedTrajectoryBuilder>(
        trajectory_options, sensor_collator_.get(), trajectory_id,
        expected_sensor_ids,
        CreateGlobalTrajectoryBuilder3D(
            std::move(local_trajectory_builder), trajectory_id,
            static_cast<PoseGraph3D*>(pose_graph_.get()),
            local_slam_result_callback, pose_graph_odometry_motion_filter)));
  } else {
    std::unique_ptr<LocalTrajectoryBuilder2D> local_trajectory_builder;
    if (trajectory_options.has_trajectory_builder_2d_options()) {
      local_trajectory_builder = absl::make_unique<LocalTrajectoryBuilder2D>(
          trajectory_options.trajectory_builder_2d_options(),
          SelectRangeSensorIds(expected_sensor_ids));
    }
    DCHECK(dynamic_cast<PoseGraph2D*>(pose_graph_.get()));
    trajectory_builders_.push_back(absl::make_unique<CollatedTrajectoryBuilder>(
        trajectory_options, sensor_collator_.get(), trajectory_id,
        expected_sensor_ids,
        CreateGlobalTrajectoryBuilder2D(
            std::move(local_trajectory_builder), trajectory_id,
            static_cast<PoseGraph2D*>(pose_graph_.get()),
            local_slam_result_callback, pose_graph_odometry_motion_filter)));
  }
  MaybeAddPureLocalizationTrimmer(trajectory_id, trajectory_options,
                                  pose_graph_.get());

  if (trajectory_options.has_initial_trajectory_pose()) {
    const auto& initial_trajectory_pose =
        trajectory_options.initial_trajectory_pose();
    pose_graph_->SetInitialTrajectoryPose(
        trajectory_id, initial_trajectory_pose.to_trajectory_id(),
        transform::ToRigid3(initial_trajectory_pose.relative_pose()),
        common::FromUniversal(initial_trajectory_pose.timestamp()));
  }
  proto::TrajectoryBuilderOptionsWithSensorIds options_with_sensor_ids_proto;
  for (const auto& sensor_id : expected_sensor_ids) {
    *options_with_sensor_ids_proto.add_sensor_id() = ToProto(sensor_id);
  }
  *options_with_sensor_ids_proto.mutable_trajectory_builder_options() =
      trajectory_options;
  all_trajectory_builder_options_.push_back(options_with_sensor_ids_proto);
  CHECK_EQ(trajectory_builders_.size(), all_trajectory_builder_options_.size());
  return trajectory_id;
}

分析cartographer经常需我们跳来跳去，现在跳到这一层分析就可以了，不管跳到哪一层。我们主要分析的是函数的声明类型以及返回值。
这其中又调用了：

          SelectRangeSensorIds(expected_sensor_ids));

std::vector<std::string> SelectRangeSensorIds(
    const std::set<MapBuilder::SensorId>& expected_sensor_ids) {
  std::vector<std::string> range_sensor_ids;
  for (const MapBuilder::SensorId& sensor_id : expected_sensor_ids) {
    if (sensor_id.type == MapBuilder::SensorId::SensorType::RANGE) {
      range_sensor_ids.push_back(sensor_id.id);
    }
  }
  return range_sensor_ids;
}

也就是说使用3D轨迹构建器一定会使用雷达。
接下来是CreateGlobalTrajectoryBuilder3D

std::unique_ptr<TrajectoryBuilderInterface> CreateGlobalTrajectoryBuilder3D(
    std::unique_ptr<LocalTrajectoryBuilder3D> local_trajectory_builder,
    const int trajectory_id, mapping::PoseGraph3D* const pose_graph,
    const TrajectoryBuilderInterface::LocalSlamResultCallback&
        local_slam_result_callback,
    const absl::optional<MotionFilter>& pose_graph_odometry_motion_filter) {
  return absl::make_unique<
      GlobalTrajectoryBuilder<LocalTrajectoryBuilder3D, mapping::PoseGraph3D>>(
      std::move(local_trajectory_builder), trajectory_id, pose_graph,
      local_slam_result_callback, pose_graph_odometry_motion_filter);
}

这期间又构造了这个类

 GlobalTrajectoryBuilder<LocalTrajectoryBuilder3D, mapping::PoseGraph3D>>

然后又调用了

  MaybeAddPureLocalizationTrimmer(trajectory_id, trajectory_options,
                                  pose_graph_.get());

void MaybeAddPureLocalizationTrimmer(
    const int trajectory_id,
    const proto::TrajectoryBuilderOptions& trajectory_options,
    PoseGraph* pose_graph) {
  if (trajectory_options.pure_localization()) {
    LOG(WARNING)
        << "'TrajectoryBuilderOptions::pure_localization' field is deprecated. "
           "Use 'TrajectoryBuilderOptions::pure_localization_trimmer' instead.";
    pose_graph->AddTrimmer(absl::make_unique<PureLocalizationTrimmer>(
        trajectory_id, 3 /* max_submaps_to_keep */));
    return;
  }
  if (trajectory_options.has_pure_localization_trimmer()) {
    pose_graph->AddTrimmer(absl::make_unique<PureLocalizationTrimmer>(
        trajectory_id,
        trajectory_options.pure_localization_trimmer().max_submaps_to_keep()));
  }
}

继续往下读，发现又调用了

pose_graph_->SetInitialTrajectoryPose

void PoseGraph3D::SetInitialTrajectoryPose(const int from_trajectory_id,
                                           const int to_trajectory_id,
                                           const transform::Rigid3d& pose,
                                           const common::Time time) {
  absl::MutexLock locker(&mutex_);
  data_.initial_trajectory_poses[from_trajectory_id] =
      InitialTrajectoryPose{to_trajectory_id, pose, time};
}

其实就是设置初始轨迹的配置
我们再调回去node类的添加轨迹函数，发现它调用了

void MapBuilderBridge::OnLocalSlamResult(
    const int trajectory_id, const ::cartographer::common::Time time,
    const Rigid3d local_pose,
    ::cartographer::sensor::RangeData range_data_in_local) {
  std::shared_ptr<const LocalTrajectoryData::LocalSlamData> local_slam_data =
      std::make_shared<LocalTrajectoryData::LocalSlamData>(
          LocalTrajectoryData::LocalSlamData{time, local_pose,
                                             std::move(range_data_in_local)});
  absl::MutexLock lock(&mutex_);
  local_slam_data_[trajectory_id] = std::move(local_slam_data);
}

这个也比较简单，就是构建局部的SLAM。
那么map_builder_bridge_.AddTrajectory(expected_sensor_ids, options);这个函数就分析完了，下一篇分析 AddExtrapolator(trajectory_id, options);这个函数。