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：
1 void LocalTrajectoryBuilder::InitializeExtrapolator(const common::Time time) 2 { 3 if (extrapolator_ != nullptr) { 4 return; 5 } 6 // We derive velocities from poses which are at least 1 ms apart for numerical 7 // stability. Usually poses known to the extrapolator will be further apart 8 // in time and thus the last two are used. 9 constexpr double kExtrapolationEstimationTimeSec = 0.001; 10 // TODO(gaschler): Consider using InitializeWithImu as 3D does. 11 extrapolator_ = common::make_unique<mapping::PoseExtrapolator>( 12 ::cartographer::common::FromSeconds(kExtrapolationEstimationTimeSec), 13 options_.imu_gravity_time_constant()); 14 extrapolator_->AddPose(time, transform::Rigid3d::Identity()); 15 }
PoseExtrapolator::InitializeWithImu方法：
1 std::unique_ptr<PoseExtrapolator> PoseExtrapolator::InitializeWithImu( 2 const common::Duration pose_queue_duration, 3 const double imu_gravity_time_constant, const sensor::ImuData& imu_data) 4 { 5 auto extrapolator = common::make_unique<PoseExtrapolator>(pose_queue_duration, imu_gravity_time_constant); 6 extrapolator->AddImuData(imu_data); 7 extrapolator->imu_tracker_ =common::make_unique<ImuTracker>(imu_gravity_time_constant, imu_data.time); 8 extrapolator->imu_tracker_->AddImuLinearAccelerationObservation( 9 imu_data.linear_acceleration); 10 extrapolator->imu_tracker_->AddImuAngularVelocityObservation( 11 imu_data.angular_velocity); 12 extrapolator->imu_tracker_->Advance(imu_data.time); 13 extrapolator->AddPose(imu_data.time,transform::Rigid3d::Rotation(extrapolator->imu_tracker_->orientation())); 14 return extrapolator; 15 }
LocalTrajectoryBuilder的AddImuData和AddOdometryData方法不赘述。
1 void LocalTrajectoryBuilder::AddImuData(const sensor::ImuData& imu_data) { 2 CHECK(options_.use_imu_data()) << "An unexpected IMU packet was added."; 3 InitializeExtrapolator(imu_data.time); 4 extrapolator_->AddImuData(imu_data); 5 } 6 7 void LocalTrajectoryBuilder::AddOdometryData( 8 const sensor::OdometryData& odometry_data) { 9 if (extrapolator_ == nullptr) { 10 // Until we've initialized the extrapolator we cannot add odometry data. 11 LOG(INFO) << "Extrapolator not yet initialized."; 12 return; 13 } 14 extrapolator_->AddOdometryData(odometry_data); 15 }
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。
1 std::unique_ptr<LocalTrajectoryBuilder::MatchingResult> 2 LocalTrajectoryBuilder::AddRangeData(const common::Time time, 3 const sensor::TimedRangeData& range_data) 4 { 5 // Initialize extrapolator now if we do not ever use an IMU. 6 if (!options_.use_imu_data()) 7 { 8 InitializeExtrapolator(time); 9 } 10 if (extrapolator_ == nullptr) 11 { 12 // Until we've initialized the extrapolator with our first IMU message, we 13 // cannot compute the orientation of the rangefinder. 14 LOG(INFO) << "Extrapolator not yet initialized."; 15 return nullptr; 16 } 17 18 CHECK(!range_data.returns.empty()); 19 CHECK_EQ(range_data.returns.back()[3], 0); 20 const common::Time time_first_point = 21 time + common::FromSeconds(range_data.returns.front()[3]); 22 if (time_first_point < extrapolator_->GetLastPoseTime()) { 23 LOG(INFO) << "Extrapolator is still initializing."; 24 return nullptr; 25 } 26 27 std::vector<transform::Rigid3f> range_data_poses; 28 range_data_poses.reserve(range_data.returns.size()); 29 for (const Eigen::Vector4f& hit : range_data.returns) { 30 const common::Time time_point = time + common::FromSeconds(hit[3]); 31 range_data_poses.push_back( 32 extrapolator_->ExtrapolatePose(time_point).cast<float>()); 33 } 34 35 if (num_accumulated_ == 0) { 36 // 'accumulated_range_data_.origin' is uninitialized until the last 37 // accumulation. 38 accumulated_range_data_ = sensor::RangeData{{}, {}, {}}; 39 } 40 41 // Drop any returns below the minimum range and convert returns beyond the 42 // maximum range into misses. 43 for (size_t i = 0; i < range_data.returns.size(); ++i) { 44 const Eigen::Vector4f& hit = range_data.returns[i]; 45 const Eigen::Vector3f origin_in_local = 46 range_data_poses[i] * range_data.origin; 47 const Eigen::Vector3f hit_in_local = range_data_poses[i] * hit.head<3>(); 48 const Eigen::Vector3f delta = hit_in_local - origin_in_local; 49 const float range = delta.norm(); 50 if (range >= options_.min_range()) { 51 if (range <= options_.max_range()) { 52 accumulated_range_data_.returns.push_back(hit_in_local); 53 } else { 54 accumulated_range_data_.misses.push_back( 55 origin_in_local + 56 options_.missing_data_ray_length() / range * delta); 57 } 58 } 59 } 60 ++num_accumulated_; 61 62 if (num_accumulated_ >= options_.num_accumulated_range_data()) { 63 num_accumulated_ = 0; 64 const transform::Rigid3d gravity_alignment = transform::Rigid3d::Rotation( 65 extrapolator_->EstimateGravityOrientation(time)); 66 accumulated_range_data_.origin = 67 range_data_poses.back() * range_data.origin; 68 return AddAccumulatedRangeData( 69 time, 70 TransformToGravityAlignedFrameAndFilter( 71 gravity_alignment.cast<float>() * range_data_poses.back().inverse(), 72 accumulated_range_data_), 73 gravity_alignment); 74 } 75 return nullptr; 76 }
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。
1 std::unique_ptr<LocalTrajectoryBuilder::MatchingResult> 2 LocalTrajectoryBuilder::AddAccumulatedRangeData( 3 const common::Time time, 4 const sensor::RangeData& gravity_aligned_range_data, 5 const transform::Rigid3d& gravity_alignment) 6 { 7 if (gravity_aligned_range_data.returns.empty()) 8 { 9 LOG(WARNING) << "Dropped empty horizontal range data."; 10 return nullptr; 11 } 12 13 // Computes a gravity aligned pose prediction. 14 const transform::Rigid3d non_gravity_aligned_pose_prediction = 15 extrapolator_->ExtrapolatePose(time); 16 const transform::Rigid2d pose_prediction = transform::Project2D( 17 non_gravity_aligned_pose_prediction * gravity_alignment.inverse()); 18 19 // local map frame <- gravity-aligned frame 20 std::unique_ptr<transform::Rigid2d> pose_estimate_2d = 21 ScanMatch(time, pose_prediction, gravity_aligned_range_data); 22 if (pose_estimate_2d == nullptr) 23 { 24 LOG(WARNING) << "Scan matching failed."; 25 return nullptr; 26 } 27 const transform::Rigid3d pose_estimate = 28 transform::Embed3D(*pose_estimate_2d) * gravity_alignment; 29 extrapolator_->AddPose(time, pose_estimate); 30 31 sensor::RangeData range_data_in_local = 32 TransformRangeData(gravity_aligned_range_data, 33 transform::Embed3D(pose_estimate_2d->cast<float>())); 34 std::unique_ptr<InsertionResult> insertion_result = 35 InsertIntoSubmap(time, range_data_in_local, gravity_aligned_range_data, 36 pose_estimate, gravity_alignment.rotation()); 37 return common::make_unique<MatchingResult>( 38 MatchingResult{time, pose_estimate, std::move(range_data_in_local), 39 std::move(insertion_result)}); 40 } 41 42 std::unique_ptr<LocalTrajectoryBuilder::InsertionResult> 43 LocalTrajectoryBuilder::InsertIntoSubmap( 44 const common::Time time, const sensor::RangeData& range_data_in_local, 45 const sensor::RangeData& gravity_aligned_range_data, 46 const transform::Rigid3d& pose_estimate, 47 const Eigen::Quaterniond& gravity_alignment) 48 { 49 if (motion_filter_.IsSimilar(time, pose_estimate)) 50 { 51 return nullptr; 52 } 53 54 // Querying the active submaps must be done here before calling 55 // InsertRangeData() since the queried values are valid for next insertion. 56 std::vector<std::shared_ptr<const Submap>> insertion_submaps; 57 for (const std::shared_ptr<Submap>& submap : active_submaps_.submaps()) 58 { 59 insertion_submaps.push_back(submap); 60 } 61 active_submaps_.InsertRangeData(range_data_in_local); 62 63 sensor::AdaptiveVoxelFilter adaptive_voxel_filter( 64 options_.loop_closure_adaptive_voxel_filter_options()); 65 const sensor::PointCloud filtered_gravity_aligned_point_cloud = 66 adaptive_voxel_filter.Filter(gravity_aligned_range_data.returns); 67 68 return common::make_unique<InsertionResult>(InsertionResult{ 69 std::make_shared<const mapping::TrajectoryNode::Data>( 70 mapping::TrajectoryNode::Data{ 71 time, 72 gravity_alignment, 73 filtered_gravity_aligned_point_cloud, 74 {}, // 'high_resolution_point_cloud' is only used in 3D. 75 {}, // 'low_resolution_point_cloud' is only used in 3D. 76 {}, // 'rotational_scan_matcher_histogram' is only used in 3D. 77 pose_estimate}), 78 std::move(insertion_submaps)}); 79 }
（2）RealTimeCorrelativeScanMatcher类，实时的扫描匹配，用的相关分析方法。