113 lines
4.2 KiB
C++
113 lines
4.2 KiB
C++
/*
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* Copyright 2016 The Cartographer Authors
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef CARTOGRAPHER_MAPPING_TRAJECTORY_BUILDER_H_
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#define CARTOGRAPHER_MAPPING_TRAJECTORY_BUILDER_H_
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#include <functional>
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#include <memory>
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#include <string>
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#include "cartographer/common/make_unique.h"
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#include "cartographer/common/port.h"
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#include "cartographer/common/time.h"
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#include "cartographer/kalman_filter/pose_tracker.h"
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#include "cartographer/mapping/submaps.h"
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#include "cartographer/sensor/data.h"
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#include "cartographer/sensor/laser.h"
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#include "cartographer/sensor/point_cloud.h"
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namespace cartographer {
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namespace mapping {
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// This interface is used for both 2D and 3D SLAM.
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class TrajectoryBuilder {
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public:
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// Represents a newly computed pose. Each of the following steps in the pose
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// estimation pipeline are provided for debugging:
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//
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// 1. UKF prediction
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// 2. Absolute pose observation (e.g. from scan matching)
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// 3. UKF estimate after integrating any measurements
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//
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// Finally, 'pose' is the end-user visualization of orientation and
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// 'point_cloud' is the point cloud, in the local map frame.
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struct PoseEstimate {
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PoseEstimate() = default;
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PoseEstimate(common::Time time,
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const kalman_filter::PoseAndCovariance& prediction,
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const kalman_filter::PoseAndCovariance& observation,
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const kalman_filter::PoseAndCovariance& estimate,
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const transform::Rigid3d& pose,
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const sensor::PointCloud& point_cloud)
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: time(time),
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prediction(prediction),
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observation(observation),
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estimate(estimate),
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pose(pose),
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point_cloud(point_cloud) {}
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common::Time time = common::Time::min();
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kalman_filter::PoseAndCovariance prediction = {
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transform::Rigid3d::Identity(), kalman_filter::PoseCovariance::Zero()};
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kalman_filter::PoseAndCovariance observation = {
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transform::Rigid3d::Identity(), kalman_filter::PoseCovariance::Zero()};
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kalman_filter::PoseAndCovariance estimate = {
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transform::Rigid3d::Identity(), kalman_filter::PoseCovariance::Zero()};
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transform::Rigid3d pose = transform::Rigid3d::Identity();
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sensor::PointCloud point_cloud;
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};
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TrajectoryBuilder() {}
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virtual ~TrajectoryBuilder() {}
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TrajectoryBuilder(const TrajectoryBuilder&) = delete;
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TrajectoryBuilder& operator=(const TrajectoryBuilder&) = delete;
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virtual const Submaps* submaps() const = 0;
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virtual const PoseEstimate& pose_estimate() const = 0;
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virtual void AddSensorData(const string& sensor_id,
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std::unique_ptr<sensor::Data> data) = 0;
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void AddRangefinderData(const string& sensor_id, common::Time time,
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const Eigen::Vector3f& origin,
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const sensor::PointCloud& ranges) {
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AddSensorData(sensor_id,
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common::make_unique<sensor::Data>(
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time, sensor::Data::Rangefinder{origin, ranges}));
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}
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void AddImuData(const string& sensor_id, common::Time time,
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const Eigen::Vector3d& linear_acceleration,
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const Eigen::Vector3d& angular_velocity) {
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AddSensorData(sensor_id, common::make_unique<sensor::Data>(
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time, sensor::Data::Imu{linear_acceleration,
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angular_velocity}));
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}
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void AddOdometerData(const string& sensor_id, common::Time time,
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const transform::Rigid3d& odometer_pose) {
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AddSensorData(sensor_id,
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common::make_unique<sensor::Data>(time, odometer_pose));
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}
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};
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} // namespace mapping
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} // namespace cartographer
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#endif // CARTOGRAPHER_MAPPING_TRAJECTORY_BUILDER_H_
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