C++ Cartographer源码中关于传感器的数据传递实现

void Node::LaunchSubscribers(const TrajectoryOptions& options,
                             const int trajectory_id);

  // laser_scan 的订阅与注册回调函数, 多个laser_scan 的topic 共用同一个回调函数
  for (const std::string& topic :
       ComputeRepeatedTopicNames(kLaserScanTopic, options.num_laser_scans)) {
    subscribers_[trajectory_id].push_back(
        {SubscribeWithHandler<sensor_msgs::LaserScan>(
             &Node::HandleLaserScanMessage, trajectory_id, topic, &node_handle_,
             this),
         topic});
  }
  // multi_echo_laser_scans的订阅与注册回调函数
  for (const std::string& topic : ComputeRepeatedTopicNames(
           kMultiEchoLaserScanTopic, options.num_multi_echo_laser_scans)) {
    subscribers_[trajectory_id].push_back(
        {SubscribeWithHandler<sensor_msgs::MultiEchoLaserScan>(
             &Node::HandleMultiEchoLaserScanMessage, trajectory_id, topic,
             &node_handle_, this),
         topic});
  }

// 调用SensorBridge的传感器处理函数进行数据处理
void Node::HandleLaserScanMessage(const int trajectory_id,
                                  const std::string& sensor_id,
                                  const sensor_msgs::LaserScan::ConstPtr& msg) {
  absl::MutexLock lock(&mutex_);
  // 根据配置,是否将传感器数据跳过
  if (!sensor_samplers_.at(trajectory_id).rangefinder_sampler.Pulse()) {
    return;
  }
  map_builder_bridge_.sensor_bridge(trajectory_id)
      ->HandleLaserScanMessage(sensor_id, msg);
}

// 处理LaserScan数据, 先转成点云,再传入trajectory_builder_
void SensorBridge::HandleLaserScanMessage(
    const std::string& sensor_id, const sensor_msgs::LaserScan::ConstPtr& msg) {
  carto::sensor::PointCloudWithIntensities point_cloud;
  carto::common::Time time;
  std::tie(point_cloud, time) = ToPointCloudWithIntensities(*msg);
  HandleLaserScan(sensor_id, time, msg->header.frame_id, point_cloud);
}

std::tuple<::cartographer::sensor::PointCloudWithIntensities,
           ::cartographer::common::Time> 

    const size_t start_index =
        points.points.size() * i / num_subdivisions_per_laser_scan_;
    const size_t end_index =
        points.points.size() * (i + 1) / num_subdivisions_per_laser_scan_;

    if (it != sensor_to_previous_subdivision_time_.end() &&
        // 上一段点云的时间不应该大于等于这一段点云的时间
        it->second >= subdivision_time) {
      LOG(WARNING) << "Ignored subdivision of a LaserScan message from sensor "
                   << sensor_id << " because previous subdivision time "
                   << it->second << " is not before current subdivision time "
                   << subdivision_time;
      continue;
    }
    // 更新对应sensor_id的时间戳
    sensor_to_previous_subdivision_time_[sensor_id] = subdivision_time;
    // 检查点云的时间
    for (auto& point : subdivision) {
      point.time -= time_to_subdivision_end;
    }
    CHECK_EQ(subdivision.back().time, 0.f);

void Node::HandleImuMessage(const int trajectory_id,
                            const std::string& sensor_id,
                            const sensor_msgs::Imu::ConstPtr& msg) {
  absl::MutexLock lock(&mutex_);
  if (!sensor_samplers_.at(trajectory_id).imu_sampler.Pulse()) {
    return;
  }
  auto sensor_bridge_ptr = map_builder_bridge_.sensor_bridge(trajectory_id);
  auto imu_data_ptr = sensor_bridge_ptr->ToImuData(msg);
  // extrapolators_使用里程计数据进行位姿预测
  if (imu_data_ptr != nullptr) {
    extrapolators_.at(trajectory_id).AddImuData(*imu_data_ptr);
  }
  sensor_bridge_ptr->HandleImuMessage(sensor_id, msg);
}

const auto sensor_to_tracking = tf_bridge_.LookupToTracking(
      time, CheckNoLeadingSlash(msg->header.frame_id));

  // 通过这个坐标变换 乘以 之后的gps数据,就相当于减去了一个固定的坐标,从而得到了gps数据间的相对坐标变换
  trajectory_builder_->AddSensorData(
      sensor_id, carto::sensor::FixedFramePoseData{
                     time, absl::optional<Rigid3d>(Rigid3d::Translation(
                               ecef_to_local_frame_.value() *
                               LatLongAltToEcef(msg->latitude, msg->longitude,
                                                msg->altitude)))});