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Devel: new feature msckf_vio

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matlabbe
2018-07-14 20:40:44 -04:00
parent 89a0eb506b
commit 15e09cd0a8
10 changed files with 1764 additions and 6 deletions
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3
corelib/include/rtabmap/core/Odometry.h
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@@ -51,7 +51,8 @@ public:
kTypeDVO = 4,
kTypeORBSLAM2 = 5,
kTypeOkvis = 6,
kTypeLOAM = 7
kTypeLOAM = 7,
kTypeMSCKF = 8
};
public:

62
corelib/include/rtabmap/core/OdometryMSCKF.h Normal file
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@@ -0,0 +1,62 @@
/*
Copyright (c) 2010-2016, Mathieu Labbe - IntRoLab - Universite de Sherbrooke
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
* Neither the name of the Universite de Sherbrooke nor the
names of its contributors may be used to endorse or promote products
derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY
DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#ifndef ODOMETRYMSCKF_H_
#define ODOMETRYMSCKF_H_
#include <rtabmap/core/Odometry.h>
namespace rtabmap {
class ImageProcessorNoROS;
class MsckfVioNoROS;
class RTABMAP_EXP OdometryMSCKF : public Odometry
{
public:
OdometryMSCKF(const rtabmap::ParametersMap & parameters = rtabmap::ParametersMap());
virtual ~OdometryMSCKF();
virtual void reset(const Transform & initialPose = Transform::getIdentity());
virtual Odometry::Type getType() {return Odometry::kTypeMSCKF;}
virtual bool canProcessRawImages() const {return true;}
private:
virtual Transform computeTransform(SensorData & image, const Transform & guess = Transform(), OdometryInfo * info = 0);
private:
#ifdef RTABMAP_MSCKF_VIO
ImageProcessorNoROS * imageProcessor_;
MsckfVioNoROS * msckf_;
IMU lastImu_;
ParametersMap parameters_;
#endif
};
}
#endif /* ODOMETRYMSCKF_H_ */

28
corelib/include/rtabmap/core/Parameters.h
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@@ -499,6 +499,34 @@ class RTABMAP_EXP Parameters
RTABMAP_PARAM(OdomLOAM, AngVar, float, 0.01, "Angular output variance.");
RTABMAP_PARAM(OdomLOAM, LocalMapping, bool, true, "Local mapping. It adds more time to compute odometry, but accuracy is significantly improved.");
// Odometry MSCKF_VIO
RTABMAP_PARAM(OdomMSCKF, GridRow, int, 4, "");
RTABMAP_PARAM(OdomMSCKF, GridCol, int, 4, "");
RTABMAP_PARAM(OdomMSCKF, GridMinFeatureNum, int, 2, "");
RTABMAP_PARAM(OdomMSCKF, GridMaxFeatureNum, int, 4, "");
RTABMAP_PARAM(OdomMSCKF, PyramidLevels, int, 3, "");
RTABMAP_PARAM(OdomMSCKF, PatchSize, int, 31, "");
RTABMAP_PARAM(OdomMSCKF, FastThreshold, int, 20, "");
RTABMAP_PARAM(OdomMSCKF, MaxIteration, int, 30, "");
RTABMAP_PARAM(OdomMSCKF, TrackPrecision, double, 0.01, "");
RTABMAP_PARAM(OdomMSCKF, RansacThreshold, double, 3, "");
RTABMAP_PARAM(OdomMSCKF, StereoThreshold, double, 3, "");
RTABMAP_PARAM(OdomMSCKF, PositionStdThreshold, double, 8.0, "");
RTABMAP_PARAM(OdomMSCKF, RotationThreshold, double, 0.2618, "");
RTABMAP_PARAM(OdomMSCKF, TranslationThreshold, double, 0.4, "");
RTABMAP_PARAM(OdomMSCKF, TrackingRateThreshold, double, 0.5, "");
RTABMAP_PARAM(OdomMSCKF, OptTranslationThreshold, double, 0.2, "");
RTABMAP_PARAM(OdomMSCKF, NoiseGyro, double, 0.001, "");
RTABMAP_PARAM(OdomMSCKF, NoiseAcc, double, 0.01, "");
RTABMAP_PARAM(OdomMSCKF, NoiseGyroBias, double, 0.001, "");
RTABMAP_PARAM(OdomMSCKF, NoiseAccBias, double, 0.01, "");
RTABMAP_PARAM(OdomMSCKF, NoiseFeature, double, 0.01, "");
RTABMAP_PARAM(OdomMSCKF, InitCovVel, double, 0.25, "");
RTABMAP_PARAM(OdomMSCKF, InitCovGyroBias, double, 0.0001, "");
RTABMAP_PARAM(OdomMSCKF, InitCovAccBias, double, 0.01, "");
RTABMAP_PARAM(OdomMSCKF, InitCovExRot, double, 0.00030462, "");
RTABMAP_PARAM(OdomMSCKF, InitCovExTrans, double, 0.0001, "");
// Common registration parameters
RTABMAP_PARAM(Reg, RepeatOnce, bool, true, "Do a second registration with the output of the first registration as guess. Only done if no guess was provided for the first registration (like on loop closure). It can be useful if the registration approach used can use a guess to get better matches.");
RTABMAP_PARAM(Reg, Strategy, int, 0, "0=Vis, 1=Icp, 2=VisIcp");

12
corelib/src/CMakeLists.txt
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@@ -69,6 +69,7 @@ SET(SRC_FILES
OdometryOkvis.cpp
OdometryORBSLAM2.cpp
OdometryLOAM.cpp
OdometryMSCKF.cpp
IMUThread.cpp
@@ -379,6 +380,17 @@ IF(okvis_FOUND)
)
ENDIF(okvis_FOUND)
IF(msckf_vio_FOUND)
SET(INCLUDE_DIRS
${msckf_vio_INCLUDE_DIRS}
${INCLUDE_DIRS}
)
SET(LIBRARIES
${msckf_vio_LIBRARIES}
${LIBRARIES}
)
ENDIF(msckf_vio_FOUND)
IF(ORB_SLAM2_FOUND)
SET(INCLUDE_DIRS
${ORB_SLAM2_INCLUDE_DIRS} #before so that g2o includes are taken from ORB_SLAM2 directory before the official g2o one

831
corelib/src/OdometryMSCKF.cpp Normal file
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@@ -0,0 +1,831 @@
/*
Copyright (c) 2010-2016, Mathieu Labbe - IntRoLab - Universite de Sherbrooke
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
* Neither the name of the Universite de Sherbrooke nor the
names of its contributors may be used to endorse or promote products
derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY
DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#include "rtabmap/core/OdometryMSCKF.h"
#include "rtabmap/core/OdometryInfo.h"
#include "rtabmap/core/util3d_transforms.h"
#include "rtabmap/utilite/ULogger.h"
#include "rtabmap/utilite/UTimer.h"
#include "rtabmap/utilite/UStl.h"
#include "rtabmap/utilite/UThread.h"
#ifdef RTABMAP_MSCKF_VIO
#include <msckf_vio/image_processor.h>
#include <msckf_vio/msckf_vio.h>
#include <msckf_vio/math_utils.hpp>
#include <eigen_conversions/eigen_msg.h>
#include <boost/math/distributions/chi_squared.hpp>
#endif
namespace rtabmap {
#ifdef RTABMAP_MSCKF_VIO
class ImageProcessorNoROS: public msckf_vio::ImageProcessor
{
public:
ImageProcessorNoROS(
const ParametersMap & parameters_in,
const Transform & imuLocalTransform,
const StereoCameraModel & model,
bool rectified)
{
// Camera calibration parameters
if(model.left().D_raw().cols == 6)
{
//equidistant
cam0_distortion_model = "equidistant";
cam0_distortion_coeffs[0] = rectified?0:model.left().D_raw().at<double>(0,0);
cam0_distortion_coeffs[1] = rectified?0:model.left().D_raw().at<double>(0,1);
cam0_distortion_coeffs[2] = rectified?0:model.left().D_raw().at<double>(0,4);
cam0_distortion_coeffs[3] = rectified?0:model.left().D_raw().at<double>(0,5);
}
else
{
//radtan
cam0_distortion_model = "radtan";
cam0_distortion_coeffs[0] = rectified?0:model.left().D_raw().at<double>(0,0);
cam0_distortion_coeffs[1] = rectified?0:model.left().D_raw().at<double>(0,1);
cam0_distortion_coeffs[2] = rectified?0:model.left().D_raw().at<double>(0,2);
cam0_distortion_coeffs[3] = rectified?0:model.left().D_raw().at<double>(0,3);
}
if(model.right().D_raw().cols == 6)
{
//equidistant
cam1_distortion_model = "equidistant";
cam1_distortion_coeffs[0] = rectified?0:model.right().D_raw().at<double>(0,0);
cam1_distortion_coeffs[1] = rectified?0:model.right().D_raw().at<double>(0,1);
cam1_distortion_coeffs[2] = rectified?0:model.right().D_raw().at<double>(0,4);
cam1_distortion_coeffs[3] = rectified?0:model.right().D_raw().at<double>(0,5);
}
else
{
//radtan
cam1_distortion_model = "radtan";
cam1_distortion_coeffs[0] = rectified?0:model.right().D_raw().at<double>(0,0);
cam1_distortion_coeffs[1] = rectified?0:model.right().D_raw().at<double>(0,1);
cam1_distortion_coeffs[2] = rectified?0:model.right().D_raw().at<double>(0,2);
cam1_distortion_coeffs[3] = rectified?0:model.right().D_raw().at<double>(0,3);
}
cam0_resolution[0] = model.left().imageWidth();
cam0_resolution[1] = model.left().imageHeight();
cam1_resolution[0] = model.right().imageWidth();
cam1_resolution[1] = model.right().imageHeight();
cam0_intrinsics[0] = model.left().fx();
cam0_intrinsics[1] = model.left().fy();
cam0_intrinsics[2] = model.left().cx();
cam0_intrinsics[3] = model.left().cy();
cam1_intrinsics[0] = model.right().fx();
cam1_intrinsics[1] = model.right().fy();
cam1_intrinsics[2] = model.right().cx();
cam1_intrinsics[3] = model.right().cy();
Transform imuCam = imuLocalTransform.inverse() * model.localTransform();
cv::Mat T_imu_cam0 = imuCam.dataMatrix();
cv::Matx33d R_imu_cam0(T_imu_cam0(cv::Rect(0,0,3,3)));
cv::Vec3d t_imu_cam0 = T_imu_cam0(cv::Rect(3,0,1,3));
R_cam0_imu = R_imu_cam0.t();
t_cam0_imu = -R_imu_cam0.t() * t_imu_cam0;
Transform cam0cam1;
if(rectified)
{
cam0cam1 = Transform(1, 0, 0, 0,
0, 1, 0, model.baseline(),
0, 0, 1, 0);
}
else
{
cam0cam1 = model.stereoTransform();
}
cv::Mat T_cam0_cam1 = cam0cam1.dataMatrix();
cv::Mat T_imu_cam1 = T_cam0_cam1 * T_imu_cam0;
cv::Matx33d R_imu_cam1(T_imu_cam1(cv::Rect(0,0,3,3)));
cv::Vec3d t_imu_cam1 = T_imu_cam1(cv::Rect(3,0,1,3));
R_cam1_imu = R_imu_cam1.t();
t_cam1_imu = -R_imu_cam1.t() * t_imu_cam1;
// Processor parameters
// get all OdomMSCFK group to make sure all parameters are set
ParametersMap parameters = Parameters::getDefaultParameters("OdomMSCKF");
uInsert(parameters, parameters_in);
Parameters::parse(parameters, Parameters::kOdomMSCKFGridRow(), processor_config.grid_row); //4
Parameters::parse(parameters, Parameters::kOdomMSCKFGridCol(), processor_config.grid_col); //4
Parameters::parse(parameters, Parameters::kOdomMSCKFGridMinFeatureNum(), processor_config.grid_min_feature_num); //2
Parameters::parse(parameters, Parameters::kOdomMSCKFGridMaxFeatureNum(), processor_config.grid_max_feature_num); //4
Parameters::parse(parameters, Parameters::kOdomMSCKFPyramidLevels(), processor_config.pyramid_levels); //3
Parameters::parse(parameters, Parameters::kOdomMSCKFPatchSize(), processor_config.patch_size); //31
Parameters::parse(parameters, Parameters::kOdomMSCKFFastThreshold(), processor_config.fast_threshold); //20
Parameters::parse(parameters, Parameters::kOdomMSCKFMaxIteration(), processor_config.max_iteration); //30
Parameters::parse(parameters, Parameters::kOdomMSCKFTrackPrecision(), processor_config.track_precision); //0.01
Parameters::parse(parameters, Parameters::kOdomMSCKFRansacThreshold(), processor_config.ransac_threshold); //3
Parameters::parse(parameters, Parameters::kOdomMSCKFStereoThreshold(), processor_config.stereo_threshold); //3
UINFO("===========================================");
UINFO("cam0_resolution: %d, %d",
cam0_resolution[0], cam0_resolution[1]);
UINFO("cam0_intrinscs: %f, %f, %f, %f",
cam0_intrinsics[0], cam0_intrinsics[1],
cam0_intrinsics[2], cam0_intrinsics[3]);
UINFO("cam0_distortion_model: %s",
cam0_distortion_model.c_str());
UINFO("cam0_distortion_coefficients: %f, %f, %f, %f",
cam0_distortion_coeffs[0], cam0_distortion_coeffs[1],
cam0_distortion_coeffs[2], cam0_distortion_coeffs[3]);
UINFO("cam1_resolution: %d, %d",
cam1_resolution[0], cam1_resolution[1]);
UINFO("cam1_intrinscs: %f, %f, %f, %f",
cam1_intrinsics[0], cam1_intrinsics[1],
cam1_intrinsics[2], cam1_intrinsics[3]);
UINFO("cam1_distortion_model: %s",
cam1_distortion_model.c_str());
UINFO("cam1_distortion_coefficients: %f, %f, %f, %f",
cam1_distortion_coeffs[0], cam1_distortion_coeffs[1],
cam1_distortion_coeffs[2], cam1_distortion_coeffs[3]);
std::cout << R_imu_cam0 << std::endl;
std::cout << t_imu_cam0.t() << std::endl;
UINFO("grid_row: %d",
processor_config.grid_row);
UINFO("grid_col: %d",
processor_config.grid_col);
UINFO("grid_min_feature_num: %d",
processor_config.grid_min_feature_num);
UINFO("grid_max_feature_num: %d",
processor_config.grid_max_feature_num);
UINFO("pyramid_levels: %d",
processor_config.pyramid_levels);
UINFO("patch_size: %d",
processor_config.patch_size);
UINFO("fast_threshold: %d",
processor_config.fast_threshold);
UINFO("max_iteration: %d",
processor_config.max_iteration);
UINFO("track_precision: %f",
processor_config.track_precision);
UINFO("ransac_threshold: %f",
processor_config.ransac_threshold);
UINFO("stereo_threshold: %f",
processor_config.stereo_threshold);
UINFO("===========================================");
}
virtual ~ImageProcessorNoROS() {}
msckf_vio::CameraMeasurementPtr stereoCallback2(
const sensor_msgs::ImageConstPtr& cam0_img,
const sensor_msgs::ImageConstPtr& cam1_img) {
//cout << "==================================" << endl;
// Get the current image.
cam0_curr_img_ptr = cv_bridge::toCvShare(cam0_img,
sensor_msgs::image_encodings::MONO8);
cam1_curr_img_ptr = cv_bridge::toCvShare(cam1_img,
sensor_msgs::image_encodings::MONO8);
// Build the image pyramids once since they're used at multiple places
createImagePyramids();
// Detect features in the first frame.
if (is_first_img) {
ros::Time start_time = ros::Time::now();
initializeFirstFrame();
//UINFO("Detection time: %f",
// (ros::Time::now()-start_time).toSec());
is_first_img = false;
// Draw results.
start_time = ros::Time::now();
drawFeaturesStereo();
//UINFO("Draw features: %f",
// (ros::Time::now()-start_time).toSec());
} else {
// Track the feature in the previous image.
ros::Time start_time = ros::Time::now();
trackFeatures();
//UINFO("Tracking time: %f",
// (ros::Time::now()-start_time).toSec());
// Add new features into the current image.
start_time = ros::Time::now();
addNewFeatures();
//UINFO("Addition time: %f",
// (ros::Time::now()-start_time).toSec());
// Add new features into the current image.
start_time = ros::Time::now();
pruneGridFeatures();
//UINFO("Prune grid features: %f",
// (ros::Time::now()-start_time).toSec());
// Draw results.
start_time = ros::Time::now();
drawFeaturesStereo();
//UINFO("Draw features: %f",
// (ros::Time::now()-start_time).toSec());
}
//ros::Time start_time = ros::Time::now();
//updateFeatureLifetime();
//UINFO("Statistics: %f",
// (ros::Time::now()-start_time).toSec());
// Publish features in the current image.
ros::Time start_time = ros::Time::now();
msckf_vio::CameraMeasurementPtr measurements = publish();
//UINFO("Publishing: %f",
// (ros::Time::now()-start_time).toSec());
// Update the previous image and previous features.
cam0_prev_img_ptr = cam0_curr_img_ptr;
prev_features_ptr = curr_features_ptr;
std::swap(prev_cam0_pyramid_, curr_cam0_pyramid_);
// Initialize the current features to empty vectors.
curr_features_ptr.reset(new GridFeatures());
for (int code = 0; code <
processor_config.grid_row*processor_config.grid_col; ++code) {
(*curr_features_ptr)[code] = std::vector<FeatureMetaData>(0);
}
return measurements;
}
msckf_vio::CameraMeasurementPtr publish() {
// Publish features.
msckf_vio::CameraMeasurementPtr feature_msg_ptr(new msckf_vio::CameraMeasurement);
feature_msg_ptr->header.stamp = cam0_curr_img_ptr->header.stamp;
std::vector<FeatureIDType> curr_ids(0);
std::vector<cv::Point2f> curr_cam0_points(0);
std::vector<cv::Point2f> curr_cam1_points(0);
for (const auto& grid_features : (*curr_features_ptr)) {
for (const auto& feature : grid_features.second) {
curr_ids.push_back(feature.id);
curr_cam0_points.push_back(feature.cam0_point);
curr_cam1_points.push_back(feature.cam1_point);
}
}
std::vector<cv::Point2f> curr_cam0_points_undistorted(0);
std::vector<cv::Point2f> curr_cam1_points_undistorted(0);
undistortPoints(
curr_cam0_points, cam0_intrinsics, cam0_distortion_model,
cam0_distortion_coeffs, curr_cam0_points_undistorted);
undistortPoints(
curr_cam1_points, cam1_intrinsics, cam1_distortion_model,
cam1_distortion_coeffs, curr_cam1_points_undistorted);
for (unsigned int i = 0; i < curr_ids.size(); ++i) {
feature_msg_ptr->features.push_back(msckf_vio::FeatureMeasurement());
feature_msg_ptr->features[i].id = curr_ids[i];
feature_msg_ptr->features[i].u0 = curr_cam0_points_undistorted[i].x;
feature_msg_ptr->features[i].v0 = curr_cam0_points_undistorted[i].y;
feature_msg_ptr->features[i].u1 = curr_cam1_points_undistorted[i].x;
feature_msg_ptr->features[i].v1 = curr_cam1_points_undistorted[i].y;
}
//feature_pub.publish(feature_msg_ptr);
// Publish tracking info.
/*TrackingInfoPtr tracking_info_msg_ptr(new TrackingInfo());
tracking_info_msg_ptr->header.stamp = cam0_curr_img_ptr->header.stamp;
tracking_info_msg_ptr->before_tracking = before_tracking;
tracking_info_msg_ptr->after_tracking = after_tracking;
tracking_info_msg_ptr->after_matching = after_matching;
tracking_info_msg_ptr->after_ransac = after_ransac;
tracking_info_pub.publish(tracking_info_msg_ptr);*/
return feature_msg_ptr;
}
};
class MsckfVioNoROS: public msckf_vio::MsckfVio
{
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
MsckfVioNoROS(const ParametersMap & parameters_in,
const Transform & imuLocalTransform,
const StereoCameraModel & model,
bool rectified)
{
// get all OdomMSCFK group to make sure all parameters are set
ParametersMap parameters = Parameters::getDefaultParameters("OdomMSCKF");
uInsert(parameters, parameters_in);
// Frame id
publish_tf = false;
frame_rate = 40.0;
Parameters::parse(parameters, Parameters::kOdomMSCKFPositionStdThreshold(), position_std_threshold); //8.0
Parameters::parse(parameters, Parameters::kOdomMSCKFRotationThreshold(), rotation_threshold); //0.2618
Parameters::parse(parameters, Parameters::kOdomMSCKFTranslationThreshold(), translation_threshold); //0.4
Parameters::parse(parameters, Parameters::kOdomMSCKFTrackingRateThreshold(), tracking_rate_threshold); //0.5
// Feature optimization parameters
Parameters::parse(parameters, Parameters::kOdomMSCKFOptTranslationThreshold(), msckf_vio::Feature::optimization_config.translation_threshold); //0.2
// Noise related parameters
Parameters::parse(parameters, Parameters::kOdomMSCKFNoiseGyro(), msckf_vio::IMUState::gyro_noise); //0.001
Parameters::parse(parameters, Parameters::kOdomMSCKFNoiseAcc(), msckf_vio::IMUState::acc_noise); //0.01
Parameters::parse(parameters, Parameters::kOdomMSCKFNoiseGyroBias(), msckf_vio::IMUState::gyro_bias_noise); //0.001
Parameters::parse(parameters, Parameters::kOdomMSCKFNoiseAccBias(), msckf_vio::IMUState::acc_bias_noise); //0.01
Parameters::parse(parameters, Parameters::kOdomMSCKFNoiseFeature(), msckf_vio::Feature::observation_noise); //0.01
// Use variance instead of standard deviation.
msckf_vio::IMUState::gyro_noise *= msckf_vio::IMUState::gyro_noise;
msckf_vio::IMUState::acc_noise *= msckf_vio::IMUState::acc_noise;
msckf_vio::IMUState::gyro_bias_noise *= msckf_vio::IMUState::gyro_bias_noise;
msckf_vio::IMUState::acc_bias_noise *= msckf_vio::IMUState::acc_bias_noise;
msckf_vio::Feature::observation_noise *= msckf_vio::Feature::observation_noise;
// Set the initial IMU state.
// The intial orientation and position will be set to the origin
// implicitly. But the initial velocity and bias can be
// set by parameters.
// TODO: is it reasonable to set the initial bias to 0?
//Parameters::parse(parameters, "initial_state/velocity/x", state_server.imu_state.velocity(0)); //0.0
//Parameters::parse(parameters, "initial_state/velocity/y", state_server.imu_state.velocity(1)); //0.0
//Parameters::parse(parameters, "initial_state/velocity/z", state_server.imu_state.velocity(2)); //0.0
// The initial covariance of orientation and position can be
// set to 0. But for velocity, bias and extrinsic parameters,
// there should be nontrivial uncertainty.
double gyro_bias_cov, acc_bias_cov, velocity_cov;
Parameters::parse(parameters, Parameters::kOdomMSCKFInitCovVel(), velocity_cov); //0.25
Parameters::parse(parameters, Parameters::kOdomMSCKFInitCovGyroBias(), gyro_bias_cov); //1e-4
Parameters::parse(parameters, Parameters::kOdomMSCKFInitCovAccBias(), acc_bias_cov); //1e-2
double extrinsic_rotation_cov, extrinsic_translation_cov;
Parameters::parse(parameters, Parameters::kOdomMSCKFInitCovExRot(), extrinsic_rotation_cov); //3.0462e-4
Parameters::parse(parameters, Parameters::kOdomMSCKFInitCovExTrans(), extrinsic_translation_cov); //1e-4
state_server.state_cov = Eigen::MatrixXd::Zero(21, 21);
for (int i = 3; i < 6; ++i)
state_server.state_cov(i, i) = gyro_bias_cov;
for (int i = 6; i < 9; ++i)
state_server.state_cov(i, i) = velocity_cov;
for (int i = 9; i < 12; ++i)
state_server.state_cov(i, i) = acc_bias_cov;
for (int i = 15; i < 18; ++i)
state_server.state_cov(i, i) = extrinsic_rotation_cov;
for (int i = 18; i < 21; ++i)
state_server.state_cov(i, i) = extrinsic_translation_cov;
// Transformation offsets between the frames involved.
Transform imuCam = imuLocalTransform.inverse() * model.localTransform();
Eigen::Isometry3d T_imu_cam0(imuCam.toEigen4d());
Eigen::Isometry3d T_cam0_imu = T_imu_cam0.inverse();
state_server.imu_state.R_imu_cam0 = T_cam0_imu.linear().transpose();
state_server.imu_state.t_cam0_imu = T_cam0_imu.translation();
Transform cam0cam1;
if(rectified)
{
cam0cam1 = Transform(1, 0, 0, 0,
0, 1, 0, model.baseline(),
0, 0, 1, 0);
}
else
{
cam0cam1 = model.stereoTransform();
}
msckf_vio::CAMState::T_cam0_cam1 = cam0cam1.toEigen3d().matrix();
msckf_vio::IMUState::T_imu_body = imuLocalTransform.toEigen3d().matrix();
// Maximum number of camera states to be stored
nh.param<int>("max_cam_state_size", max_cam_state_size, 30);
UINFO("===========================================");
UINFO("fixed frame id: %s", fixed_frame_id.c_str());
UINFO("child frame id: %s", child_frame_id.c_str());
UINFO("publish tf: %d", publish_tf);
UINFO("frame rate: %f", frame_rate);
UINFO("position std threshold: %f", position_std_threshold);
UINFO("Keyframe rotation threshold: %f", rotation_threshold);
UINFO("Keyframe translation threshold: %f", translation_threshold);
UINFO("Keyframe tracking rate threshold: %f", tracking_rate_threshold);
UINFO("gyro noise: %.10f", msckf_vio::IMUState::gyro_noise);
UINFO("gyro bias noise: %.10f", msckf_vio::IMUState::gyro_bias_noise);
UINFO("acc noise: %.10f", msckf_vio::IMUState::acc_noise);
UINFO("acc bias noise: %.10f", msckf_vio::IMUState::acc_bias_noise);
UINFO("observation noise: %.10f", msckf_vio::Feature::observation_noise);
UINFO("initial velocity: %f, %f, %f",
state_server.imu_state.velocity(0),
state_server.imu_state.velocity(1),
state_server.imu_state.velocity(2));
UINFO("initial gyro bias cov: %f", gyro_bias_cov);
UINFO("initial acc bias cov: %f", acc_bias_cov);
UINFO("initial velocity cov: %f", velocity_cov);
UINFO("initial extrinsic rotation cov: %f",
extrinsic_rotation_cov);
UINFO("initial extrinsic translation cov: %f",
extrinsic_translation_cov);
std::cout << T_imu_cam0.linear() << std::endl;
std::cout << T_imu_cam0.translation().transpose() << std::endl;
UINFO("max camera state #: %d", max_cam_state_size);
UINFO("===========================================");
//if (!loadParameters()) return false;
//UINFO("Finish loading ROS parameters...");
// Initialize state server
state_server.continuous_noise_cov =
Eigen::Matrix<double, 12, 12>::Zero();
state_server.continuous_noise_cov.block<3, 3>(0, 0) =
Eigen::Matrix3d::Identity()*msckf_vio::IMUState::gyro_noise;
state_server.continuous_noise_cov.block<3, 3>(3, 3) =
Eigen::Matrix3d::Identity()*msckf_vio::IMUState::gyro_bias_noise;
state_server.continuous_noise_cov.block<3, 3>(6, 6) =
Eigen::Matrix3d::Identity()*msckf_vio::IMUState::acc_noise;
state_server.continuous_noise_cov.block<3, 3>(9, 9) =
Eigen::Matrix3d::Identity()*msckf_vio::IMUState::acc_bias_noise;
// Initialize the chi squared test table with confidence
// level 0.95.
for (int i = 1; i < 100; ++i) {
boost::math::chi_squared chi_squared_dist(i);
chi_squared_test_table[i] =
boost::math::quantile(chi_squared_dist, 0.05);
}
// if (!createRosIO()) return false;
//UINFO("Finish creating ROS IO...");
}
virtual ~MsckfVioNoROS() {}
nav_msgs::Odometry featureCallback2(
const msckf_vio::CameraMeasurementConstPtr& msg) {
nav_msgs::Odometry odom;
// Return if the gravity vector has not been set.
if (!is_gravity_set) return odom;
// Start the system if the first image is received.
// The frame where the first image is received will be
// the origin.
if (is_first_img) {
is_first_img = false;
state_server.imu_state.time = msg->header.stamp.toSec();
}
//static double max_processing_time = 0.0;
static int critical_time_cntr = 0;
double processing_start_time = ros::Time::now().toSec();
// Propogate the IMU state.
// that are received before the image msg.
ros::Time start_time = ros::Time::now();
batchImuProcessing(msg->header.stamp.toSec());
//double imu_processing_time = (
// ros::Time::now()-start_time).toSec();
// Augment the state vector.
start_time = ros::Time::now();
stateAugmentation(msg->header.stamp.toSec());
//double state_augmentation_time = (
// ros::Time::now()-start_time).toSec();
// Add new observations for existing features or new
// features in the map server.
start_time = ros::Time::now();
addFeatureObservations(msg);
//double add_observations_time = (
// ros::Time::now()-start_time).toSec();
// Perform measurement update if necessary.
start_time = ros::Time::now();
removeLostFeatures();
double remove_lost_features_time = (
ros::Time::now()-start_time).toSec();
start_time = ros::Time::now();
pruneCamStateBuffer();
double prune_cam_states_time = (
ros::Time::now()-start_time).toSec();
// Publish the odometry.
start_time = ros::Time::now();
odom = publish(msg->header.stamp);
//double publish_time = (
// ros::Time::now()-start_time).toSec();
// Reset the system if necessary.
onlineReset();
double processing_end_time = ros::Time::now().toSec();
double processing_time =
processing_end_time - processing_start_time;
if (processing_time > 1.0/frame_rate) {
++critical_time_cntr;
UINFO("\033[1;31mTotal processing time %f/%d...\033[0m",
processing_time, critical_time_cntr);
//printf("IMU processing time: %f/%f\n",
// imu_processing_time, imu_processing_time/processing_time);
//printf("State augmentation time: %f/%f\n",
// state_augmentation_time, state_augmentation_time/processing_time);
//printf("Add observations time: %f/%f\n",
// add_observations_time, add_observations_time/processing_time);
printf("Remove lost features time: %f/%f\n",
remove_lost_features_time, remove_lost_features_time/processing_time);
printf("Remove camera states time: %f/%f\n",
prune_cam_states_time, prune_cam_states_time/processing_time);
//printf("Publish time: %f/%f\n",
// publish_time, publish_time/processing_time);
}
return odom;
}
nav_msgs::Odometry publish(const ros::Time& time) {
// Convert the IMU frame to the body frame.
const msckf_vio::IMUState& imu_state = state_server.imu_state;
Eigen::Isometry3d T_i_w = Eigen::Isometry3d::Identity();
T_i_w.linear() = msckf_vio::quaternionToRotation(imu_state.orientation).transpose();
T_i_w.translation() = imu_state.position;
Eigen::Isometry3d T_b_w = msckf_vio::IMUState::T_imu_body * T_i_w *
msckf_vio::IMUState::T_imu_body.inverse();
Eigen::Vector3d body_velocity =
msckf_vio::IMUState::T_imu_body.linear() * imu_state.velocity;
// Publish tf
/*if (publish_tf) {
tf::Transform T_b_w_tf;
tf::transformEigenToTF(T_b_w, T_b_w_tf);
tf_pub.sendTransform(tf::StampedTransform(
T_b_w_tf, time, fixed_frame_id, child_frame_id));
}*/
// Publish the odometry
nav_msgs::Odometry odom_msg;
odom_msg.header.stamp = time;
odom_msg.header.frame_id = fixed_frame_id;
odom_msg.child_frame_id = child_frame_id;
tf::poseEigenToMsg(T_b_w, odom_msg.pose.pose);
tf::vectorEigenToMsg(body_velocity, odom_msg.twist.twist.linear);
// Convert the covariance.
Eigen::Matrix3d P_oo = state_server.state_cov.block<3, 3>(0, 0);
Eigen::Matrix3d P_op = state_server.state_cov.block<3, 3>(0, 12);
Eigen::Matrix3d P_po = state_server.state_cov.block<3, 3>(12, 0);
Eigen::Matrix3d P_pp = state_server.state_cov.block<3, 3>(12, 12);
Eigen::Matrix<double, 6, 6> P_imu_pose = Eigen::Matrix<double, 6, 6>::Zero();
P_imu_pose << P_pp, P_po, P_op, P_oo;
Eigen::Matrix<double, 6, 6> H_pose = Eigen::Matrix<double, 6, 6>::Zero();
H_pose.block<3, 3>(0, 0) = msckf_vio::IMUState::T_imu_body.linear();
H_pose.block<3, 3>(3, 3) = msckf_vio::IMUState::T_imu_body.linear();
Eigen::Matrix<double, 6, 6> P_body_pose = H_pose *
P_imu_pose * H_pose.transpose();
for (int i = 0; i < 6; ++i)
for (int j = 0; j < 6; ++j)
odom_msg.pose.covariance[6*i+j] = P_body_pose(i, j);
// Construct the covariance for the velocity.
Eigen::Matrix3d P_imu_vel = state_server.state_cov.block<3, 3>(6, 6);
Eigen::Matrix3d H_vel = msckf_vio::IMUState::T_imu_body.linear();
Eigen::Matrix3d P_body_vel = H_vel * P_imu_vel * H_vel.transpose();
for (int i = 0; i < 3; ++i)
for (int j = 0; j < 3; ++j)
odom_msg.twist.covariance[i*6+j] = P_body_vel(i, j);
// odom_pub.publish(odom_msg);
// Publish the 3D positions of the features that
// has been initialized.
/*pcl::PointCloud<pcl::PointXYZ>::Ptr feature_msg_ptr(
new pcl::PointCloud<pcl::PointXYZ>());
feature_msg_ptr->header.frame_id = fixed_frame_id;
feature_msg_ptr->height = 1;
for (const auto& item : map_server) {
const auto& feature = item.second;
if (feature.is_initialized) {
Vector3d feature_position =
IMUState::T_imu_body.linear() * feature.position;
feature_msg_ptr->points.push_back(pcl::PointXYZ(
feature_position(0), feature_position(1), feature_position(2)));
}
}
feature_msg_ptr->width = feature_msg_ptr->points.size();
feature_pub.publish(feature_msg_ptr);*/
return odom_msg;
}
};
#endif
OdometryMSCKF::OdometryMSCKF(const ParametersMap & parameters) :
Odometry(parameters)
#ifdef RTABMAP_MSCKF_VIO
,
imageProcessor_(0),
msckf_(0),
parameters_(parameters)
#endif
{
}
OdometryMSCKF::~OdometryMSCKF()
{
UDEBUG("");
#ifdef RTABMAP_MSCKF_VIO
if(imageProcessor_)
{
delete imageProcessor_;
}
if(msckf_)
{
delete msckf_;
}
#endif
}
void OdometryMSCKF::reset(const Transform & initialPose)
{
Odometry::reset(initialPose);
#ifdef RTABMAP_MSCKF_VIO
if(imageProcessor_)
{
delete imageProcessor_;
imageProcessor_ = 0;
}
if(msckf_)
{
delete msckf_;
msckf_ = 0;
}
lastImu_ = IMU();
#endif
}
// return not null transform if odometry is correctly computed
Transform OdometryMSCKF::computeTransform(
SensorData & data,
const Transform & guess,
OdometryInfo * info)
{
UDEBUG("");
Transform t;
#ifdef RTABMAP_MSCKF_VIO
UTimer timer;
if(!data.imu().empty())
{
UDEBUG("IMU update stamp=%f acc=%f %f %f gyr=%f %f %f", data.stamp(),
data.imu().linearAcceleration()[0],
data.imu().linearAcceleration()[1],
data.imu().linearAcceleration()[2],
data.imu().angularVelocity()[0],
data.imu().angularVelocity()[1],
data.imu().angularVelocity()[2]);
if(imageProcessor_ && msckf_)
{
sensor_msgs::ImuPtr msg(new sensor_msgs::Imu);
msg->angular_velocity.x = data.imu().angularVelocity()[0];
msg->angular_velocity.y = data.imu().angularVelocity()[1];
msg->angular_velocity.z = data.imu().angularVelocity()[2];
msg->linear_acceleration.x = data.imu().linearAcceleration()[0];
msg->linear_acceleration.y = data.imu().linearAcceleration()[1];
msg->linear_acceleration.z = data.imu().linearAcceleration()[2];
msg->header.stamp.fromSec(data.stamp());
imageProcessor_->imuCallback(msg);
msckf_->imuCallback(msg);
}
else
{
UWARN("Ignoring IMU, waiting for an image to initialize...");
lastImu_ = data.imu();
}
}
if(!data.imageRaw().empty() && !data.rightRaw().empty())
{
if(data.stereoCameraModel().isValidForProjection())
{
if(msckf_ == 0)
{
UDEBUG("Initialization");
if(lastImu_.empty())
{
UWARN("Ignoring Image, waiting for imu to initialize...");
return t;
}
imageProcessor_ = new ImageProcessorNoROS(
parameters_,
lastImu_.localTransform(),
data.stereoCameraModel(),
this->imagesAlreadyRectified());
msckf_ = new MsckfVioNoROS(
parameters_,
lastImu_.localTransform(),
data.stereoCameraModel(),
this->imagesAlreadyRectified());
}
// Convert to ROS
cv_bridge::CvImage cam0;
cv_bridge::CvImage cam1;
cam0.header.stamp.fromSec(data.stamp());
cam1.header.stamp.fromSec(data.stamp());
if(data.imageRaw().type() == CV_8UC3)
{
cv::cvtColor(data.imageRaw(), cam0.image, CV_BGR2GRAY);
}
else
{
cam0.image = data.imageRaw();
}
if(data.rightRaw().type() == CV_8UC3)
{
cv::cvtColor(data.rightRaw(), cam1.image, CV_BGR2GRAY);
}
else
{
cam1.image = data.rightRaw();
}
sensor_msgs::ImagePtr cam0Msg(new sensor_msgs::Image);
sensor_msgs::ImagePtr cam1Msg(new sensor_msgs::Image);
cam0.toImageMsg(*cam0Msg);
cam1.toImageMsg(*cam1Msg);
//msckf_vio::FeatureMeasurementPtr measurementsConst = measurements;
nav_msgs::Odometry odom = msckf_->featureCallback2(
imageProcessor_->stereoCallback2(cam0Msg, cam1Msg));
Transform p = Transform(
odom.pose.pose.position.x,
odom.pose.pose.position.y,
odom.pose.pose.position.z,
odom.pose.pose.orientation.x,
odom.pose.pose.orientation.y,
odom.pose.pose.orientation.z,
odom.pose.pose.orientation.w);
if(!p.isNull())
{
// make it incremental
t = this->getPose().inverse()*p;
if(info)
{
info->reg.covariance = cv::Mat(6,6,CV_64FC1, odom.twist.covariance.elems).clone();
}
}
UINFO("Odom update time = %fs p=%s", timer.elapsed(), p.prettyPrint().c_str());
}
}
#else
UERROR("RTAB-Map is not built with MSCKF_VIO support! Select another visual odometry approach.");
#endif
return t;
}
} // namespace rtabmap

12
corelib/src/Parameters.cpp
View File

@@ -679,6 +679,18 @@ ParametersMap Parameters::parseArguments(int argc, char * argv[], bool onlyParam
{
ignore = true;
}
#endif
#ifndef RTABMAP_LOAM
if(group.compare("OdomLOAM") == 0)
{
ignore = true;
}
#endif
#ifndef RTABMAP_MSCKF_VIO
if(group.compare("OdomMSCKF") == 0)
{
ignore = true;
}
#endif
if(!ignore)
{