Cartographer源码阅读(6)：LocalTrajectoryBuilder和PoseExtrapolator

LocalTrajectoryBuilder意思是局部轨迹的构建，下面的类图中方法的参数没有画进去。

注意其中的三个类：PoseExtrapolator类，RealTimeCorrelativeScanMatcher类和CeresScanMatcher类。

（1）PoseExtrapolator类（如下图），Node类和LocalTrajectoryBuilder类都有用到PoseExtrapolator对象，好像两者之间并没有什么关系？

LocalTrajectoryBuilder中的PoseExtrapolator对象类似于运动模型。

（Node类中的可能是为了发布位姿信息用的，单独进行了位姿推算。先不管了。）

PoseExtrapolator的构造函数 VS 通过IMU初始化InitializeWithImu方法。

在LocalTrajectoryBuilder::InitializeExtrapolator中对其构造函数的调用：

void LocalTrajectoryBuilder::InitializeExtrapolator(const common::Time time)
{
  if (extrapolator_ != nullptr) {
    return;
  }
  // We derive velocities from poses which are at least 1 ms apart for numerical
  // stability. Usually poses known to the extrapolator will be further apart
  // in time and thus the last two are used.
  constexpr double kExtrapolationEstimationTimeSec = 0.001;
  // TODO(gaschler): Consider using InitializeWithImu as 3D does.
  extrapolator_ = common::make_unique<mapping::PoseExtrapolator>(
      ::cartographer::common::FromSeconds(kExtrapolationEstimationTimeSec),
      options_.imu_gravity_time_constant());
  extrapolator_->AddPose(time, transform::Rigid3d::Identity());
}

PoseExtrapolator::InitializeWithImu方法：

std::unique_ptr<PoseExtrapolator> PoseExtrapolator::InitializeWithImu(
    const common::Duration pose_queue_duration,
    const double imu_gravity_time_constant, const sensor::ImuData& imu_data)
{
  auto extrapolator = common::make_unique<PoseExtrapolator>(pose_queue_duration, imu_gravity_time_constant);
  extrapolator->AddImuData(imu_data);
  extrapolator->imu_tracker_ =common::make_unique<ImuTracker>(imu_gravity_time_constant, imu_data.time);
  extrapolator->imu_tracker_->AddImuLinearAccelerationObservation(
      imu_data.linear_acceleration);
  extrapolator->imu_tracker_->AddImuAngularVelocityObservation(
      imu_data.angular_velocity);
  extrapolator->imu_tracker_->Advance(imu_data.time);
  extrapolator->AddPose(imu_data.time,transform::Rigid3d::Rotation(extrapolator->imu_tracker_->orientation()));
  return extrapolator;
}

 LocalTrajectoryBuilder的AddImuData和AddOdometryData方法不赘述。

void LocalTrajectoryBuilder::AddImuData(const sensor::ImuData& imu_data) {
  CHECK(options_.use_imu_data()) << "An unexpected IMU packet was added.";
  InitializeExtrapolator(imu_data.time);
  extrapolator_->AddImuData(imu_data);
}

void LocalTrajectoryBuilder::AddOdometryData(
    const sensor::OdometryData& odometry_data) {
  if (extrapolator_ == nullptr) {
    // Until we've initialized the extrapolator we cannot add odometry data.
    LOG(INFO) << "Extrapolator not yet initialized.";
    return;
  }
  extrapolator_->AddOdometryData(odometry_data);
}

如下查看LocalTrajectoryBuilder::AddRangeData方法。如果使用IMU数据，直接进入10行，如果不是用进入7行。

std::unique_ptr<LocalTrajectoryBuilder::MatchingResult>
LocalTrajectoryBuilder::AddRangeData(const common::Time time,
                                     const sensor::TimedRangeData& range_data) 
{
  // Initialize extrapolator now if we do not ever use an IMU.
  if (!options_.use_imu_data()) 
  {
    InitializeExtrapolator(time);
  }
  if (extrapolator_ == nullptr) 
  {
    // Until we've initialized the extrapolator with our first IMU message, we
    // cannot compute the orientation of the rangefinder.
    LOG(INFO) << "Extrapolator not yet initialized.";
    return nullptr;
  }

  CHECK(!range_data.returns.empty());
  CHECK_EQ(range_data.returns.back()[3], 0);
  const common::Time time_first_point =
      time + common::FromSeconds(range_data.returns.front()[3]);
  if (time_first_point < extrapolator_->GetLastPoseTime()) {
    LOG(INFO) << "Extrapolator is still initializing.";
    return nullptr;
  }

  std::vector<transform::Rigid3f> range_data_poses;
  range_data_poses.reserve(range_data.returns.size());
  for (const Eigen::Vector4f& hit : range_data.returns) {
    const common::Time time_point = time + common::FromSeconds(hit[3]);
    range_data_poses.push_back(
        extrapolator_->ExtrapolatePose(time_point).cast<float>());
  }

  if (num_accumulated_ == 0) {
    // 'accumulated_range_data_.origin' is uninitialized until the last
    // accumulation.
    accumulated_range_data_ = sensor::RangeData{{}, {}, {}};
  }

  // Drop any returns below the minimum range and convert returns beyond the
  // maximum range into misses.
  for (size_t i = 0; i < range_data.returns.size(); ++i) {
    const Eigen::Vector4f& hit = range_data.returns[i];
    const Eigen::Vector3f origin_in_local =
        range_data_poses[i] * range_data.origin;
    const Eigen::Vector3f hit_in_local = range_data_poses[i] * hit.head<3>();
    const Eigen::Vector3f delta = hit_in_local - origin_in_local;
    const float range = delta.norm();
    if (range >= options_.min_range()) {
      if (range <= options_.max_range()) {
        accumulated_range_data_.returns.push_back(hit_in_local);
      } else {
        accumulated_range_data_.misses.push_back(
            origin_in_local +
            options_.missing_data_ray_length() / range * delta);
      }
    }
  }
  ++num_accumulated_;

  if (num_accumulated_ >= options_.num_accumulated_range_data()) {
    num_accumulated_ = 0;
    const transform::Rigid3d gravity_alignment = transform::Rigid3d::Rotation(
        extrapolator_->EstimateGravityOrientation(time));
    accumulated_range_data_.origin =
        range_data_poses.back() * range_data.origin;
    return AddAccumulatedRangeData(
        time,
        TransformToGravityAlignedFrameAndFilter(
            gravity_alignment.cast<float>() * range_data_poses.back().inverse(),
            accumulated_range_data_),
        gravity_alignment);
  }
  return nullptr;
}

 接着，LocalTrajectoryBuilder::AddAccumulatedRangeData代码如下，传入的参数为3个。

const common::Time time，const sensor::RangeData& gravity_aligned_range_data, const transform::Rigid3d& gravity_alignment

重力定向，定向后的深度数据和定向矩阵。

注意下面21行代码执行了扫描匹配的ScanMatch方法，之后代码29行调用的extrapolator_->AddPose()方法：

每次扫描匹配之后执行AddPose方法。

std::unique_ptr<LocalTrajectoryBuilder::MatchingResult>
LocalTrajectoryBuilder::AddAccumulatedRangeData(
    const common::Time time,
    const sensor::RangeData& gravity_aligned_range_data,
    const transform::Rigid3d& gravity_alignment) 
{
  if (gravity_aligned_range_data.returns.empty())
  {
    LOG(WARNING) << "Dropped empty horizontal range data.";
    return nullptr;
  }

  // Computes a gravity aligned pose prediction.
  const transform::Rigid3d non_gravity_aligned_pose_prediction =
      extrapolator_->ExtrapolatePose(time);
  const transform::Rigid2d pose_prediction = transform::Project2D(
      non_gravity_aligned_pose_prediction * gravity_alignment.inverse());

  // local map frame <- gravity-aligned frame
  std::unique_ptr<transform::Rigid2d> pose_estimate_2d =
      ScanMatch(time, pose_prediction, gravity_aligned_range_data);
  if (pose_estimate_2d == nullptr) 
  {
    LOG(WARNING) << "Scan matching failed.";
    return nullptr;
  }
  const transform::Rigid3d pose_estimate =
      transform::Embed3D(*pose_estimate_2d) * gravity_alignment;
  extrapolator_->AddPose(time, pose_estimate);

  sensor::RangeData range_data_in_local =
      TransformRangeData(gravity_aligned_range_data,
                         transform::Embed3D(pose_estimate_2d->cast<float>()));
  std::unique_ptr<InsertionResult> insertion_result =
      InsertIntoSubmap(time, range_data_in_local, gravity_aligned_range_data,
                       pose_estimate, gravity_alignment.rotation());
  return common::make_unique<MatchingResult>(
      MatchingResult{time, pose_estimate, std::move(range_data_in_local),
                     std::move(insertion_result)});
}

std::unique_ptr<LocalTrajectoryBuilder::InsertionResult>
LocalTrajectoryBuilder::InsertIntoSubmap(
    const common::Time time, const sensor::RangeData& range_data_in_local,
    const sensor::RangeData& gravity_aligned_range_data,
    const transform::Rigid3d& pose_estimate,
    const Eigen::Quaterniond& gravity_alignment) 
{
  if (motion_filter_.IsSimilar(time, pose_estimate))
  {
    return nullptr;
  }

  // Querying the active submaps must be done here before calling
  // InsertRangeData() since the queried values are valid for next insertion.
  std::vector<std::shared_ptr<const Submap>> insertion_submaps;
  for (const std::shared_ptr<Submap>& submap : active_submaps_.submaps()) 
  {
    insertion_submaps.push_back(submap);
  }
  active_submaps_.InsertRangeData(range_data_in_local);

  sensor::AdaptiveVoxelFilter adaptive_voxel_filter(
      options_.loop_closure_adaptive_voxel_filter_options());
  const sensor::PointCloud filtered_gravity_aligned_point_cloud =
      adaptive_voxel_filter.Filter(gravity_aligned_range_data.returns);

  return common::make_unique<InsertionResult>(InsertionResult{
      std::make_shared<const mapping::TrajectoryNode::Data>(
          mapping::TrajectoryNode::Data{
              time,
              gravity_alignment,
              filtered_gravity_aligned_point_cloud,
              {},  // 'high_resolution_point_cloud' is only used in 3D.
              {},  // 'low_resolution_point_cloud' is only used in 3D.
              {},  // 'rotational_scan_matcher_histogram' is only used in 3D.
              pose_estimate}),
      std::move(insertion_submaps)});
}

（2）RealTimeCorrelativeScanMatcher类，实时的扫描匹配，用的相关分析方法。