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msckf_vio/src/msckf_vio.cpp

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First commit of the code
2018-01-08 20:41:37 +01:00
/*
* COPYRIGHT AND PERMISSION NOTICE
* Penn Software MSCKF_VIO
* Copyright (C) 2017 The Trustees of the University of Pennsylvania
* All rights reserved.
*/
#include <iostream>
#include <iomanip>
#include <cmath>
#include <iterator>
#include <algorithm>
#include <Eigen/SVD>
#include <Eigen/QR>
#include <Eigen/SparseCore>
#include <Eigen/SPQRSupport>
#include <boost/math/distributions/chi_squared.hpp>
#include <eigen_conversions/eigen_msg.h>
#include <tf_conversions/tf_eigen.h>
#include <sensor_msgs/PointCloud2.h>
#include <pcl_ros/point_cloud.h>
#include <pcl/point_types.h>
#include <msckf_vio/msckf_vio.h>
#include <msckf_vio/math_utils.hpp>
using namespace std;
using namespace Eigen;
namespace msckf_vio{
// Static member variables in IMUState class.
StateIDType IMUState::next_id = 0;
double IMUState::gyro_noise = 0.001;
double IMUState::acc_noise = 0.01;
double IMUState::gyro_bias_noise = 0.001;
double IMUState::acc_bias_noise = 0.01;
Vector3d IMUState::gravity = Vector3d(0, 0, -GRAVITY_ACCELERATION);
Isometry3d IMUState::T_imu_body = Isometry3d::Identity();
// Static member variables in CAMState class.
Isometry3d CAMState::T_cam0_cam1 = Isometry3d::Identity();
// Static member variables in Feature class.
FeatureIDType Feature::next_id = 0;
double Feature::observation_noise = 0.01;
Feature::OptimizationConfig Feature::optimization_config;
map<int, double> MsckfVio::chi_squared_test_table;
MsckfVio::MsckfVio(ros::NodeHandle& pnh):
is_gravity_set(false),
is_first_img(true),
nh(pnh) {
return;
}
bool MsckfVio::loadParameters() {
// Frame id
nh.param<string>("fixed_frame_id", fixed_frame_id, "world");
nh.param<string>("child_frame_id", child_frame_id, "robot");
nh.param<bool>("publish_tf", publish_tf, true);
nh.param<double>("frame_rate", frame_rate, 40.0);
nh.param<double>("position_std_threshold", position_std_threshold, 8.0);
nh.param<double>("rotation_threshold", rotation_threshold, 0.2618);
nh.param<double>("translation_threshold", translation_threshold, 0.4);
nh.param<double>("tracking_rate_threshold", tracking_rate_threshold, 0.5);
// Feature optimization parameters
nh.param<double>("feature/config/translation_threshold",
Feature::optimization_config.translation_threshold, 0.2);
// Noise related parameters
nh.param<double>("noise/gyro", IMUState::gyro_noise, 0.001);
nh.param<double>("noise/acc", IMUState::acc_noise, 0.01);
nh.param<double>("noise/gyro_bias", IMUState::gyro_bias_noise, 0.001);
nh.param<double>("noise/acc_bias", IMUState::acc_bias_noise, 0.01);
nh.param<double>("noise/feature", Feature::observation_noise, 0.01);
// Use variance instead of standard deviation.
IMUState::gyro_noise *= IMUState::gyro_noise;
IMUState::acc_noise *= IMUState::acc_noise;
IMUState::gyro_bias_noise *= IMUState::gyro_bias_noise;
IMUState::acc_bias_noise *= IMUState::acc_bias_noise;
Feature::observation_noise *= 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?
nh.param<double>("initial_state/velocity/x",
state_server.imu_state.velocity(0), 0.0);
nh.param<double>("initial_state/velocity/y",
state_server.imu_state.velocity(1), 0.0);
nh.param<double>("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;
nh.param<double>("initial_covariance/velocity",
velocity_cov, 0.25);
nh.param<double>("initial_covariance/gyro_bias",
gyro_bias_cov, 1e-4);
nh.param<double>("initial_covariance/acc_bias",
acc_bias_cov, 1e-2);
double extrinsic_rotation_cov, extrinsic_translation_cov;
nh.param<double>("initial_covariance/extrinsic_rotation_cov",
extrinsic_rotation_cov, 3.0462e-4);
nh.param<double>("initial_covariance/extrinsic_translation_cov",
extrinsic_translation_cov, 1e-4);
state_server.state_cov = 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.
Isometry3d T_imu_cam0;
vector<double> cam0_extrinsics(16);
nh.getParam("cam0/T_cam_imu", cam0_extrinsics);
T_imu_cam0.linear()(0, 0) = cam0_extrinsics[0];
T_imu_cam0.linear()(0, 1) = cam0_extrinsics[1];
T_imu_cam0.linear()(0, 2) = cam0_extrinsics[2];
T_imu_cam0.linear()(1, 0) = cam0_extrinsics[4];
T_imu_cam0.linear()(1, 1) = cam0_extrinsics[5];
T_imu_cam0.linear()(1, 2) = cam0_extrinsics[6];
T_imu_cam0.linear()(2, 0) = cam0_extrinsics[8];
T_imu_cam0.linear()(2, 1) = cam0_extrinsics[9];
T_imu_cam0.linear()(2, 2) = cam0_extrinsics[10];
T_imu_cam0.translation()(0) = cam0_extrinsics[3];
T_imu_cam0.translation()(1) = cam0_extrinsics[7];
T_imu_cam0.translation()(2) = cam0_extrinsics[11];
Isometry3d T_cam0_imu = T_imu_cam0.inverse();
Isometry3d T_imu_cam1;
vector<double> cam1_extrinsics(16);
nh.getParam("cam1/T_cam_imu", cam1_extrinsics);
T_imu_cam1.linear()(0, 0) = cam1_extrinsics[0];
T_imu_cam1.linear()(0, 1) = cam1_extrinsics[1];
T_imu_cam1.linear()(0, 2) = cam1_extrinsics[2];
T_imu_cam1.linear()(1, 0) = cam1_extrinsics[4];
T_imu_cam1.linear()(1, 1) = cam1_extrinsics[5];
T_imu_cam1.linear()(1, 2) = cam1_extrinsics[6];
T_imu_cam1.linear()(2, 0) = cam1_extrinsics[8];
T_imu_cam1.linear()(2, 1) = cam1_extrinsics[9];
T_imu_cam1.linear()(2, 2) = cam1_extrinsics[10];
T_imu_cam1.translation()(0) = cam1_extrinsics[3];
T_imu_cam1.translation()(1) = cam1_extrinsics[7];
T_imu_cam1.translation()(2) = cam1_extrinsics[11];
Isometry3d T_cam1_imu = T_imu_cam1.inverse();
Isometry3d T_body_imu;
vector<double> imu_extrinsics(16);
nh.getParam("T_imu_body", imu_extrinsics);
T_body_imu.linear()(0, 0) = imu_extrinsics[0];
T_body_imu.linear()(0, 1) = imu_extrinsics[1];
T_body_imu.linear()(0, 2) = imu_extrinsics[2];
T_body_imu.linear()(1, 0) = imu_extrinsics[4];
T_body_imu.linear()(1, 1) = imu_extrinsics[5];
T_body_imu.linear()(1, 2) = imu_extrinsics[6];
T_body_imu.linear()(2, 0) = imu_extrinsics[8];
T_body_imu.linear()(2, 1) = imu_extrinsics[9];
T_body_imu.linear()(2, 2) = imu_extrinsics[10];
T_body_imu.translation()(0) = imu_extrinsics[3];
T_body_imu.translation()(1) = imu_extrinsics[7];
T_body_imu.translation()(2) = imu_extrinsics[11];
//CAMState::T_imu_cam0 = T_cam0_imu.inverse();
state_server.imu_state.R_imu_cam0 = T_cam0_imu.linear().transpose();
state_server.imu_state.t_cam0_imu = T_cam0_imu.translation();
CAMState::T_cam0_cam1 = T_imu_cam1 * T_cam0_imu;
IMUState::T_imu_body = T_body_imu.inverse();
// Maximum number of camera states to be stored
nh.param<int>("max_cam_state_size", max_cam_state_size, 30);
ROS_INFO("===========================================");
ROS_INFO("fixed frame id: %s", fixed_frame_id.c_str());
ROS_INFO("child frame id: %s", child_frame_id.c_str());
ROS_INFO("publish tf: %d", publish_tf);
ROS_INFO("frame rate: %f", frame_rate);
ROS_INFO("position std threshold: %f", position_std_threshold);
ROS_INFO("Keyframe rotation threshold: %f", rotation_threshold);
ROS_INFO("Keyframe translation threshold: %f", translation_threshold);
ROS_INFO("Keyframe tracking rate threshold: %f", tracking_rate_threshold);
ROS_INFO("gyro noise: %.10f", IMUState::gyro_noise);
ROS_INFO("gyro bias noise: %.10f", IMUState::gyro_bias_noise);
ROS_INFO("acc noise: %.10f", IMUState::acc_noise);
ROS_INFO("acc bias noise: %.10f", IMUState::acc_bias_noise);
ROS_INFO("observation noise: %.10f", Feature::observation_noise);
ROS_INFO("initial velocity: %f, %f, %f",
state_server.imu_state.velocity(0),
state_server.imu_state.velocity(1),
state_server.imu_state.velocity(2));
ROS_INFO("initial gyro bias cov: %f", gyro_bias_cov);
ROS_INFO("initial acc bias cov: %f", acc_bias_cov);
ROS_INFO("initial velocity cov: %f", velocity_cov);
ROS_INFO("initial extrinsic rotation cov: %f",
extrinsic_rotation_cov);
ROS_INFO("initial extrinsic translation cov: %f",
extrinsic_translation_cov);
cout << T_imu_cam1.linear() << endl;
cout << T_imu_cam1.translation().transpose() << endl;
ROS_INFO("max camera state #: %d", max_cam_state_size);
ROS_INFO("===========================================");
return true;
}
bool MsckfVio::createRosIO() {
odom_pub = nh.advertise<nav_msgs::Odometry>("odom", 10);
feature_pub = nh.advertise<sensor_msgs::PointCloud2>(
"feature_point_cloud", 10);
reset_srv = nh.advertiseService("reset",
&MsckfVio::resetCallback, this);
imu_sub = nh.subscribe("imu", 100,
&MsckfVio::imuCallback, this);
feature_sub = nh.subscribe("features", 40,
&MsckfVio::featureCallback, this);
mocap_odom_sub = nh.subscribe("mocap_odom", 10,
&MsckfVio::mocapOdomCallback, this);
mocap_odom_pub = nh.advertise<nav_msgs::Odometry>("gt_odom", 1);
return true;
}
bool MsckfVio::initialize() {
if (!loadParameters()) return false;
ROS_INFO("Finish loading ROS parameters...");
// Initialize state server
state_server.continuous_noise_cov =
Matrix<double, 12, 12>::Zero();
state_server.continuous_noise_cov.block<3, 3>(0, 0) =
Matrix3d::Identity()*IMUState::gyro_noise;
state_server.continuous_noise_cov.block<3, 3>(3, 3) =
Matrix3d::Identity()*IMUState::gyro_bias_noise;
state_server.continuous_noise_cov.block<3, 3>(6, 6) =
Matrix3d::Identity()*IMUState::acc_noise;
state_server.continuous_noise_cov.block<3, 3>(9, 9) =
Matrix3d::Identity()*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;
ROS_INFO("Finish creating ROS IO...");
return true;
}
void MsckfVio::imuCallback(
const sensor_msgs::ImuConstPtr& msg) {
// IMU msgs are pushed backed into a buffer instead of
// being processed immediately. The IMU msgs are processed
// when the next image is available, in which way, we can
// easily handle the transfer delay.
imu_msg_buffer.push_back(*msg);
if (!is_gravity_set) {
if (imu_msg_buffer.size() < 200) return;
//if (imu_msg_buffer.size() < 10) return;
initializeGravityAndBias();
is_gravity_set = true;
}
return;
}
void MsckfVio::initializeGravityAndBias() {
// Initialize gravity and gyro bias.
Vector3d sum_angular_vel = Vector3d::Zero();
Vector3d sum_linear_acc = Vector3d::Zero();
for (const auto& imu_msg : imu_msg_buffer) {
Vector3d angular_vel = Vector3d::Zero();
Vector3d linear_acc = Vector3d::Zero();
tf::vectorMsgToEigen(imu_msg.angular_velocity, angular_vel);
tf::vectorMsgToEigen(imu_msg.linear_acceleration, linear_acc);
sum_angular_vel += angular_vel;
sum_linear_acc += linear_acc;
}
state_server.imu_state.gyro_bias =
sum_angular_vel / imu_msg_buffer.size();
//IMUState::gravity =
// -sum_linear_acc / imu_msg_buffer.size();
// This is the gravity in the IMU frame.
Vector3d gravity_imu =
sum_linear_acc / imu_msg_buffer.size();
// Initialize the initial orientation, so that the estimation
// is consistent with the inertial frame.
double gravity_norm = gravity_imu.norm();
IMUState::gravity = Vector3d(0.0, 0.0, -gravity_norm);
Quaterniond q0_i_w = Quaterniond::FromTwoVectors(
gravity_imu, -IMUState::gravity);
state_server.imu_state.orientation =
rotationToQuaternion(q0_i_w.toRotationMatrix().transpose());
return;
}
bool MsckfVio::resetCallback(
std_srvs::Trigger::Request& req,
std_srvs::Trigger::Response& res) {
ROS_WARN("Start resetting msckf vio...");
// Temporarily shutdown the subscribers to prevent the
// state from updating.
feature_sub.shutdown();
imu_sub.shutdown();
// Reset the IMU state.
IMUState& imu_state = state_server.imu_state;
imu_state.time = 0.0;
imu_state.orientation = Vector4d(0.0, 0.0, 0.0, 1.0);
imu_state.position = Vector3d::Zero();
imu_state.velocity = Vector3d::Zero();
imu_state.gyro_bias = Vector3d::Zero();
imu_state.acc_bias = Vector3d::Zero();
imu_state.orientation_null = Vector4d(0.0, 0.0, 0.0, 1.0);
imu_state.position_null = Vector3d::Zero();
imu_state.velocity_null = Vector3d::Zero();
// Remove all existing camera states.
state_server.cam_states.clear();
// Reset the state covariance.
double gyro_bias_cov, acc_bias_cov, velocity_cov;
nh.param<double>("initial_covariance/velocity",
velocity_cov, 0.25);
nh.param<double>("initial_covariance/gyro_bias",
gyro_bias_cov, 1e-4);
nh.param<double>("initial_covariance/acc_bias",
acc_bias_cov, 1e-2);
double extrinsic_rotation_cov, extrinsic_translation_cov;
nh.param<double>("initial_covariance/extrinsic_rotation_cov",
extrinsic_rotation_cov, 3.0462e-4);
nh.param<double>("initial_covariance/extrinsic_translation_cov",
extrinsic_translation_cov, 1e-4);
state_server.state_cov = 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;
// Clear all exsiting features in the map.
map_server.clear();
// Clear the IMU msg buffer.
imu_msg_buffer.clear();
// Reset the starting flags.
is_gravity_set = false;
is_first_img = true;
// Restart the subscribers.
imu_sub = nh.subscribe("imu", 100,
&MsckfVio::imuCallback, this);
feature_sub = nh.subscribe("features", 40,
&MsckfVio::featureCallback, this);
// TODO: When can the reset fail?
res.success = true;
ROS_WARN("Resetting msckf vio completed...");
return true;
}
void MsckfVio::featureCallback(
const CameraMeasurementConstPtr& msg) {
// Return if the gravity vector has not been set.
if (!is_gravity_set) return;
// 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();
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;
ROS_INFO("\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;
}
void MsckfVio::mocapOdomCallback(
const nav_msgs::OdometryConstPtr& msg) {
static bool first_mocap_odom_msg = true;
// If this is the first mocap odometry messsage, set
// the initial frame.
if (first_mocap_odom_msg) {
Quaterniond orientation;
Vector3d translation;
tf::pointMsgToEigen(
msg->pose.pose.position, translation);
tf::quaternionMsgToEigen(
msg->pose.pose.orientation, orientation);
//tf::vectorMsgToEigen(
// msg->transform.translation, translation);
//tf::quaternionMsgToEigen(
// msg->transform.rotation, orientation);
mocap_initial_frame.linear() = orientation.toRotationMatrix();
mocap_initial_frame.translation() = translation;
first_mocap_odom_msg = false;
}
// Transform the ground truth.
Quaterniond orientation;
Vector3d translation;
//tf::vectorMsgToEigen(
// msg->transform.translation, translation);
//tf::quaternionMsgToEigen(
// msg->transform.rotation, orientation);
tf::pointMsgToEigen(
msg->pose.pose.position, translation);
tf::quaternionMsgToEigen(
msg->pose.pose.orientation, orientation);
Eigen::Isometry3d T_b_v_gt;
T_b_v_gt.linear() = orientation.toRotationMatrix();
T_b_v_gt.translation() = translation;
Eigen::Isometry3d T_b_w_gt = mocap_initial_frame.inverse() * T_b_v_gt;
//Eigen::Vector3d body_velocity_gt;
//tf::vectorMsgToEigen(msg->twist.twist.linear, body_velocity_gt);
//body_velocity_gt = mocap_initial_frame.linear().transpose() *
// body_velocity_gt;
// Ground truth tf.
if (publish_tf) {
tf::Transform T_b_w_gt_tf;
tf::transformEigenToTF(T_b_w_gt, T_b_w_gt_tf);
tf_pub.sendTransform(tf::StampedTransform(
T_b_w_gt_tf, msg->header.stamp, fixed_frame_id, child_frame_id+"_mocap"));
}
// Ground truth odometry.
nav_msgs::Odometry mocap_odom_msg;
mocap_odom_msg.header.stamp = msg->header.stamp;
mocap_odom_msg.header.frame_id = fixed_frame_id;
mocap_odom_msg.child_frame_id = child_frame_id+"_mocap";
tf::poseEigenToMsg(T_b_w_gt, mocap_odom_msg.pose.pose);
//tf::vectorEigenToMsg(body_velocity_gt,
// mocap_odom_msg.twist.twist.linear);
mocap_odom_pub.publish(mocap_odom_msg);
return;
}
void MsckfVio::batchImuProcessing(const double& time_bound) {
// Counter how many IMU msgs in the buffer are used.
int used_imu_msg_cntr = 0;
for (const auto& imu_msg : imu_msg_buffer) {
double imu_time = imu_msg.header.stamp.toSec();
if (imu_time < state_server.imu_state.time) {
++used_imu_msg_cntr;
continue;
}
if (imu_time > time_bound) break;
// Convert the msgs.
Vector3d m_gyro, m_acc;
tf::vectorMsgToEigen(imu_msg.angular_velocity, m_gyro);
tf::vectorMsgToEigen(imu_msg.linear_acceleration, m_acc);
// Execute process model.
processModel(imu_time, m_gyro, m_acc);
++used_imu_msg_cntr;
}
// Set the state ID for the new IMU state.
state_server.imu_state.id = IMUState::next_id++;
// Remove all used IMU msgs.
imu_msg_buffer.erase(imu_msg_buffer.begin(),
imu_msg_buffer.begin()+used_imu_msg_cntr);
return;
}
void MsckfVio::processModel(const double& time,
const Vector3d& m_gyro,
const Vector3d& m_acc) {
// Remove the bias from the measured gyro and acceleration
IMUState& imu_state = state_server.imu_state;
Vector3d gyro = m_gyro - imu_state.gyro_bias;
Vector3d acc = m_acc - imu_state.acc_bias;
double dtime = time - imu_state.time;
// Compute discrete transition and noise covariance matrix
Matrix<double, 21, 21> F = Matrix<double, 21, 21>::Zero();
Matrix<double, 21, 12> G = Matrix<double, 21, 12>::Zero();
F.block<3, 3>(0, 0) = -skewSymmetric(gyro);
F.block<3, 3>(0, 3) = -Matrix3d::Identity();
F.block<3, 3>(6, 0) = -quaternionToRotation(
imu_state.orientation).transpose()*skewSymmetric(acc);
F.block<3, 3>(6, 9) = -quaternionToRotation(
imu_state.orientation).transpose();
F.block<3, 3>(12, 6) = Matrix3d::Identity();
G.block<3, 3>(0, 0) = -Matrix3d::Identity();
G.block<3, 3>(3, 3) = Matrix3d::Identity();
G.block<3, 3>(6, 6) = -quaternionToRotation(
imu_state.orientation).transpose();
G.block<3, 3>(9, 9) = Matrix3d::Identity();
// Approximate matrix exponential to the 3rd order,
// which can be considered to be accurate enough assuming
// dtime is within 0.01s.
Matrix<double, 21, 21> Fdt = F * dtime;
Matrix<double, 21, 21> Fdt_square = Fdt * Fdt;
Matrix<double, 21, 21> Fdt_cube = Fdt_square * Fdt;
Matrix<double, 21, 21> Phi = Matrix<double, 21, 21>::Identity() +
Fdt + 0.5*Fdt_square + (1.0/6.0)*Fdt_cube;
// Propogate the state using 4th order Runge-Kutta
predictNewState(dtime, gyro, acc);
// Modify the transition matrix
Matrix3d R_kk_1 = quaternionToRotation(imu_state.orientation_null);
Phi.block<3, 3>(0, 0) =
quaternionToRotation(imu_state.orientation) * R_kk_1.transpose();
Vector3d u = R_kk_1 * IMUState::gravity;
RowVector3d s = (u.transpose()*u).inverse() * u.transpose();
Matrix3d A1 = Phi.block<3, 3>(6, 0);
Vector3d w1 = skewSymmetric(
imu_state.velocity_null-imu_state.velocity) * IMUState::gravity;
Phi.block<3, 3>(6, 0) = A1 - (A1*u-w1)*s;
Matrix3d A2 = Phi.block<3, 3>(12, 0);
Vector3d w2 = skewSymmetric(
dtime*imu_state.velocity_null+imu_state.position_null-
imu_state.position) * IMUState::gravity;
Phi.block<3, 3>(12, 0) = A2 - (A2*u-w2)*s;
// Propogate the state covariance matrix.
Matrix<double, 21, 21> Q = Phi*G*state_server.continuous_noise_cov*
G.transpose()*Phi.transpose()*dtime;
state_server.state_cov.block<21, 21>(0, 0) =
Phi*state_server.state_cov.block<21, 21>(0, 0)*Phi.transpose() + Q;
if (state_server.cam_states.size() > 0) {
state_server.state_cov.block(
0, 21, 21, state_server.state_cov.cols()-21) =
Phi * state_server.state_cov.block(
0, 21, 21, state_server.state_cov.cols()-21);
state_server.state_cov.block(
21, 0, state_server.state_cov.rows()-21, 21) =
state_server.state_cov.block(
21, 0, state_server.state_cov.rows()-21, 21) * Phi.transpose();
}
MatrixXd state_cov_fixed = (state_server.state_cov +
state_server.state_cov.transpose()) / 2.0;
state_server.state_cov = state_cov_fixed;
// Update the state correspondes to null space.
imu_state.orientation_null = imu_state.orientation;
imu_state.position_null = imu_state.position;
imu_state.velocity_null = imu_state.velocity;
// Update the state info
state_server.imu_state.time = time;
return;
}
void MsckfVio::predictNewState(const double& dt,
const Vector3d& gyro,
const Vector3d& acc) {
// TODO: Will performing the forward integration using
// the inverse of the quaternion give better accuracy?
double gyro_norm = gyro.norm();
Matrix4d Omega = Matrix4d::Zero();
Omega.block<3, 3>(0, 0) = -skewSymmetric(gyro);
Omega.block<3, 1>(0, 3) = gyro;
Omega.block<1, 3>(3, 0) = -gyro;
Vector4d& q = state_server.imu_state.orientation;
Vector3d& v = state_server.imu_state.velocity;
Vector3d& p = state_server.imu_state.position;
// Some pre-calculation
Vector4d dq_dt, dq_dt2;
if (gyro_norm > 1e-5) {
dq_dt = (cos(gyro_norm*dt*0.5)*Matrix4d::Identity() +
1/gyro_norm*sin(gyro_norm*dt*0.5)*Omega) * q;
dq_dt2 = (cos(gyro_norm*dt*0.25)*Matrix4d::Identity() +
1/gyro_norm*sin(gyro_norm*dt*0.25)*Omega) * q;
}
else {
dq_dt = (Matrix4d::Identity()+0.5*dt*Omega) *
cos(gyro_norm*dt*0.5) * q;
dq_dt2 = (Matrix4d::Identity()+0.25*dt*Omega) *
cos(gyro_norm*dt*0.25) * q;
}
Matrix3d dR_dt_transpose = quaternionToRotation(dq_dt).transpose();
Matrix3d dR_dt2_transpose = quaternionToRotation(dq_dt2).transpose();
// k1 = f(tn, yn)
Vector3d k1_v_dot = quaternionToRotation(q).transpose()*acc +
IMUState::gravity;
Vector3d k1_p_dot = v;
// k2 = f(tn+dt/2, yn+k1*dt/2)
Vector3d k1_v = v + k1_v_dot*dt/2;
Vector3d k2_v_dot = dR_dt2_transpose*acc +
IMUState::gravity;
Vector3d k2_p_dot = k1_v;
// k3 = f(tn+dt/2, yn+k2*dt/2)
Vector3d k2_v = v + k2_v_dot*dt/2;
Vector3d k3_v_dot = dR_dt2_transpose*acc +
IMUState::gravity;
Vector3d k3_p_dot = k2_v;
// k4 = f(tn+dt, yn+k3*dt)
Vector3d k3_v = v + k3_v_dot*dt;
Vector3d k4_v_dot = dR_dt_transpose*acc +
IMUState::gravity;
Vector3d k4_p_dot = k3_v;
// yn+1 = yn + dt/6*(k1+2*k2+2*k3+k4)
q = dq_dt;
quaternionNormalize(q);
v = v + dt/6*(k1_v_dot+2*k2_v_dot+2*k3_v_dot+k4_v_dot);
p = p + dt/6*(k1_p_dot+2*k2_p_dot+2*k3_p_dot+k4_p_dot);
return;
}
void MsckfVio::stateAugmentation(const double& time) {
const Matrix3d& R_i_c = state_server.imu_state.R_imu_cam0;
const Vector3d& t_c_i = state_server.imu_state.t_cam0_imu;
// Add a new camera state to the state server.
Matrix3d R_w_i = quaternionToRotation(
state_server.imu_state.orientation);
Matrix3d R_w_c = R_i_c * R_w_i;
Vector3d t_c_w = state_server.imu_state.position +
R_w_i.transpose()*t_c_i;
state_server.cam_states[state_server.imu_state.id] =
CAMState(state_server.imu_state.id);
CAMState& cam_state = state_server.cam_states[
state_server.imu_state.id];
cam_state.time = time;
cam_state.orientation = rotationToQuaternion(R_w_c);
cam_state.position = t_c_w;
cam_state.orientation_null = cam_state.orientation;
cam_state.position_null = cam_state.position;
// Update the covariance matrix of the state.
// To simplify computation, the matrix J below is the nontrivial block
// in Equation (16) in "A Multi-State Constraint Kalman Filter for Vision
// -aided Inertial Navigation".
Matrix<double, 6, 21> J = Matrix<double, 6, 21>::Zero();
J.block<3, 3>(0, 0) = R_i_c;
J.block<3, 3>(0, 15) = Matrix3d::Identity();
J.block<3, 3>(3, 0) = skewSymmetric(R_w_i.transpose()*t_c_i);
//J.block<3, 3>(3, 0) = -R_w_i.transpose()*skewSymmetric(t_c_i);
J.block<3, 3>(3, 12) = Matrix3d::Identity();
J.block<3, 3>(3, 18) = Matrix3d::Identity();
// Resize the state covariance matrix.
size_t old_rows = state_server.state_cov.rows();
size_t old_cols = state_server.state_cov.cols();
state_server.state_cov.conservativeResize(old_rows+6, old_cols+6);
// Rename some matrix blocks for convenience.
const Matrix<double, 21, 21>& P11 =
state_server.state_cov.block<21, 21>(0, 0);
const MatrixXd& P12 =
state_server.state_cov.block(0, 21, 21, old_cols-21);
// Fill in the augmented state covariance.
state_server.state_cov.block(old_rows, 0, 6, old_cols) << J*P11, J*P12;
state_server.state_cov.block(0, old_cols, old_rows, 6) =
state_server.state_cov.block(old_rows, 0, 6, old_cols).transpose();
state_server.state_cov.block<6, 6>(old_rows, old_cols) =
J * P11 * J.transpose();
// Fix the covariance to be symmetric
MatrixXd state_cov_fixed = (state_server.state_cov +
state_server.state_cov.transpose()) / 2.0;
state_server.state_cov = state_cov_fixed;
return;
}
void MsckfVio::addFeatureObservations(
const CameraMeasurementConstPtr& msg) {
StateIDType state_id = state_server.imu_state.id;
int curr_feature_num = map_server.size();
int tracked_feature_num = 0;
// Add new observations for existing features or new
// features in the map server.
for (const auto& feature : msg->features) {
if (map_server.find(feature.id) == map_server.end()) {
// This is a new feature.
map_server[feature.id] = Feature(feature.id);
map_server[feature.id].observations[state_id] =
Vector4d(feature.u0, feature.v0,
feature.u1, feature.v1);
} else {
// This is an old feature.
map_server[feature.id].observations[state_id] =
Vector4d(feature.u0, feature.v0,
feature.u1, feature.v1);
++tracked_feature_num;
}
}
tracking_rate =
static_cast<double>(tracked_feature_num) /
static_cast<double>(curr_feature_num);
return;
}
void MsckfVio::measurementJacobian(
const StateIDType& cam_state_id,
const FeatureIDType& feature_id,
Matrix<double, 4, 6>& H_x, Matrix<double, 4, 3>& H_f, Vector4d& r) {
// Prepare all the required data.
const CAMState& cam_state = state_server.cam_states[cam_state_id];
const Feature& feature = map_server[feature_id];
// Cam0 pose.
Matrix3d R_w_c0 = quaternionToRotation(cam_state.orientation);
const Vector3d& t_c0_w = cam_state.position;
// Cam1 pose.
Matrix3d R_c0_c1 = CAMState::T_cam0_cam1.linear();
Matrix3d R_w_c1 = CAMState::T_cam0_cam1.linear() * R_w_c0;
Vector3d t_c1_w = t_c0_w - R_w_c1.transpose()*CAMState::T_cam0_cam1.translation();
// 3d feature position in the world frame.
// And its observation with the stereo cameras.
const Vector3d& p_w = feature.position;
const Vector4d& z = feature.observations.find(cam_state_id)->second;
// Convert the feature position from the world frame to
// the cam0 and cam1 frame.
Vector3d p_c0 = R_w_c0 * (p_w-t_c0_w);
Vector3d p_c1 = R_w_c1 * (p_w-t_c1_w);
// Compute the Jacobians.
Matrix<double, 4, 3> dz_dpc0 = Matrix<double, 4, 3>::Zero();
dz_dpc0(0, 0) = 1 / p_c0(2);
dz_dpc0(1, 1) = 1 / p_c0(2);
dz_dpc0(0, 2) = -p_c0(0) / (p_c0(2)*p_c0(2));
dz_dpc0(1, 2) = -p_c0(1) / (p_c0(2)*p_c0(2));
Matrix<double, 4, 3> dz_dpc1 = Matrix<double, 4, 3>::Zero();
dz_dpc1(2, 0) = 1 / p_c1(2);
dz_dpc1(3, 1) = 1 / p_c1(2);
dz_dpc1(2, 2) = -p_c1(0) / (p_c1(2)*p_c1(2));
dz_dpc1(3, 2) = -p_c1(1) / (p_c1(2)*p_c1(2));
Matrix<double, 3, 6> dpc0_dxc = Matrix<double, 3, 6>::Zero();
dpc0_dxc.leftCols(3) = skewSymmetric(p_c0);
dpc0_dxc.rightCols(3) = -R_w_c0;
Matrix<double, 3, 6> dpc1_dxc = Matrix<double, 3, 6>::Zero();
dpc1_dxc.leftCols(3) = R_c0_c1 * skewSymmetric(p_c0);
dpc1_dxc.rightCols(3) = -R_w_c1;
Matrix3d dpc0_dpg = R_w_c0;
Matrix3d dpc1_dpg = R_w_c1;
H_x = dz_dpc0*dpc0_dxc + dz_dpc1*dpc1_dxc;
H_f = dz_dpc0*dpc0_dpg + dz_dpc1*dpc1_dpg;
// Modifty the measurement Jacobian to ensure
// observability constrain.
Matrix<double, 4, 6> A = H_x;
Matrix<double, 6, 1> u = Matrix<double, 6, 1>::Zero();
u.block<3, 1>(0, 0) = quaternionToRotation(
cam_state.orientation_null) * IMUState::gravity;
u.block<3, 1>(3, 0) = skewSymmetric(
p_w-cam_state.position_null) * IMUState::gravity;
H_x = A - A*u*(u.transpose()*u).inverse()*u.transpose();
H_f = -H_x.block<4, 3>(0, 3);
// Compute the residual.
r = z - Vector4d(p_c0(0)/p_c0(2), p_c0(1)/p_c0(2),
p_c1(0)/p_c1(2), p_c1(1)/p_c1(2));
return;
}
void MsckfVio::featureJacobian(
const FeatureIDType& feature_id,
const std::vector<StateIDType>& cam_state_ids,
MatrixXd& H_x, VectorXd& r) {
const auto& feature = map_server[feature_id];
// Check how many camera states in the provided camera
// id camera has actually seen this feature.
vector<StateIDType> valid_cam_state_ids(0);
for (const auto& cam_id : cam_state_ids) {
if (feature.observations.find(cam_id) ==
feature.observations.end()) continue;
valid_cam_state_ids.push_back(cam_id);
}
int jacobian_row_size = 0;
jacobian_row_size = 4 * valid_cam_state_ids.size();
MatrixXd H_xj = MatrixXd::Zero(jacobian_row_size,
21+state_server.cam_states.size()*6);
MatrixXd H_fj = MatrixXd::Zero(jacobian_row_size, 3);
VectorXd r_j = VectorXd::Zero(jacobian_row_size);
int stack_cntr = 0;
for (const auto& cam_id : valid_cam_state_ids) {
Matrix<double, 4, 6> H_xi = Matrix<double, 4, 6>::Zero();
Matrix<double, 4, 3> H_fi = Matrix<double, 4, 3>::Zero();
Vector4d r_i = Vector4d::Zero();
measurementJacobian(cam_id, feature.id, H_xi, H_fi, r_i);
auto cam_state_iter = state_server.cam_states.find(cam_id);
int cam_state_cntr = std::distance(
state_server.cam_states.begin(), cam_state_iter);
// Stack the Jacobians.
H_xj.block<4, 6>(stack_cntr, 21+6*cam_state_cntr) = H_xi;
H_fj.block<4, 3>(stack_cntr, 0) = H_fi;
r_j.segment<4>(stack_cntr) = r_i;
stack_cntr += 4;
}
// Project the residual and Jacobians onto the nullspace
// of H_fj.
JacobiSVD<MatrixXd> svd_helper(H_fj, ComputeFullU | ComputeThinV);
MatrixXd A = svd_helper.matrixU().rightCols(
jacobian_row_size - 3);
H_x = A.transpose() * H_xj;
r = A.transpose() * r_j;
return;
}
void MsckfVio::measurementUpdate(
const MatrixXd& H, const VectorXd& r) {
if (H.rows() == 0 || r.rows() == 0) return;
// Decompose the final Jacobian matrix to reduce computational
// complexity as in Equation (28), (29).
MatrixXd H_thin;
VectorXd r_thin;
if (H.rows() > H.cols()) {
// Convert H to a sparse matrix.
SparseMatrix<double> H_sparse = H.sparseView();
// Perform QR decompostion on H_sparse.
SPQR<SparseMatrix<double> > spqr_helper;
spqr_helper.setSPQROrdering(SPQR_ORDERING_NATURAL);
spqr_helper.compute(H_sparse);
MatrixXd H_temp;
VectorXd r_temp;
(spqr_helper.matrixQ().transpose() * H).evalTo(H_temp);
(spqr_helper.matrixQ().transpose() * r).evalTo(r_temp);
H_thin = H_temp.topRows(21+state_server.cam_states.size()*6);
r_thin = r_temp.head(21+state_server.cam_states.size()*6);
//HouseholderQR<MatrixXd> qr_helper(H);
//MatrixXd Q = qr_helper.householderQ();
//MatrixXd Q1 = Q.leftCols(21+state_server.cam_states.size()*6);
//H_thin = Q1.transpose() * H;
//r_thin = Q1.transpose() * r;
} else {
H_thin = H;
r_thin = r;
}
// Compute the Kalman gain.
const MatrixXd& P = state_server.state_cov;
MatrixXd S = H_thin*P*H_thin.transpose() +
Feature::observation_noise*MatrixXd::Identity(
H_thin.rows(), H_thin.rows());
//MatrixXd K_transpose = S.fullPivHouseholderQr().solve(H_thin*P);
MatrixXd K_transpose = S.ldlt().solve(H_thin*P);
MatrixXd K = K_transpose.transpose();
// Compute the error of the state.
VectorXd delta_x = K * r_thin;
// Update the IMU state.
const VectorXd& delta_x_imu = delta_x.head<21>();
if (//delta_x_imu.segment<3>(0).norm() > 0.15 ||
//delta_x_imu.segment<3>(3).norm() > 0.15 ||
delta_x_imu.segment<3>(6).norm() > 0.5 ||
//delta_x_imu.segment<3>(9).norm() > 0.5 ||
delta_x_imu.segment<3>(12).norm() > 1.0) {
printf("delta velocity: %f\n", delta_x_imu.segment<3>(6).norm());
printf("delta position: %f\n", delta_x_imu.segment<3>(12).norm());
ROS_WARN("Update change is too large.");
//return;
}
const Vector4d dq_imu =
smallAngleQuaternion(delta_x_imu.head<3>());
state_server.imu_state.orientation = quaternionMultiplication(
dq_imu, state_server.imu_state.orientation);
state_server.imu_state.gyro_bias += delta_x_imu.segment<3>(3);
state_server.imu_state.velocity += delta_x_imu.segment<3>(6);
state_server.imu_state.acc_bias += delta_x_imu.segment<3>(9);
state_server.imu_state.position += delta_x_imu.segment<3>(12);
const Vector4d dq_extrinsic =
smallAngleQuaternion(delta_x_imu.segment<3>(15));
state_server.imu_state.R_imu_cam0 = quaternionToRotation(
dq_extrinsic) * state_server.imu_state.R_imu_cam0;
state_server.imu_state.t_cam0_imu += delta_x_imu.segment<3>(18);
// Update the camera states.
auto cam_state_iter = state_server.cam_states.begin();
for (int i = 0; i < state_server.cam_states.size();
++i, ++cam_state_iter) {
const VectorXd& delta_x_cam = delta_x.segment<6>(21+i*6);
const Vector4d dq_cam = smallAngleQuaternion(delta_x_cam.head<3>());
cam_state_iter->second.orientation = quaternionMultiplication(
dq_cam, cam_state_iter->second.orientation);
cam_state_iter->second.position += delta_x_cam.tail<3>();
}
// Update state covariance.
MatrixXd I_KH = MatrixXd::Identity(K.rows(), H_thin.cols()) - K*H_thin;
//state_server.state_cov = I_KH*state_server.state_cov*I_KH.transpose() +
// K*K.transpose()*Feature::observation_noise;
state_server.state_cov = I_KH*state_server.state_cov;
// Fix the covariance to be symmetric
MatrixXd state_cov_fixed = (state_server.state_cov +
state_server.state_cov.transpose()) / 2.0;
state_server.state_cov = state_cov_fixed;
return;
}
bool MsckfVio::gatingTest(
const MatrixXd& H, const VectorXd& r, const int& dof) {
MatrixXd P1 = H * state_server.state_cov * H.transpose();
MatrixXd P2 = Feature::observation_noise *
MatrixXd::Identity(H.rows(), H.rows());
double gamma = r.transpose() * (P1+P2).ldlt().solve(r);
//cout << dof << " " << gamma << " " <<
// chi_squared_test_table[dof] << " ";
if (gamma < chi_squared_test_table[dof]) {
//cout << "passed" << endl;
return true;
} else {
//cout << "failed" << endl;
return false;
}
}
void MsckfVio::removeLostFeatures() {
// Remove the features that lost track.
// BTW, find the size the final Jacobian matrix and residual vector.
int jacobian_row_size = 0;
vector<FeatureIDType> invalid_feature_ids(0);
vector<FeatureIDType> processed_feature_ids(0);
for (auto iter = map_server.begin();
iter != map_server.end(); ++iter) {
// Rename the feature to be checked.
auto& feature = iter->second;
// Pass the features that are still being tracked.
if (feature.observations.find(state_server.imu_state.id) !=
feature.observations.end()) continue;
if (feature.observations.size() < 3) {
invalid_feature_ids.push_back(feature.id);
continue;
}
// Check if the feature can be initialized if it
// has not been.
if (!feature.is_initialized) {
if (!feature.checkMotion(state_server.cam_states)) {
invalid_feature_ids.push_back(feature.id);
continue;
} else {
if(!feature.initializePosition(state_server.cam_states)) {
invalid_feature_ids.push_back(feature.id);
continue;
}
}
}
jacobian_row_size += 4*feature.observations.size() - 3;
processed_feature_ids.push_back(feature.id);
}
//cout << "invalid/processed feature #: " <<
// invalid_feature_ids.size() << "/" <<
// processed_feature_ids.size() << endl;
//cout << "jacobian row #: " << jacobian_row_size << endl;
// Remove the features that do not have enough measurements.
for (const auto& feature_id : invalid_feature_ids)
map_server.erase(feature_id);
// Return if there is no lost feature to be processed.
if (processed_feature_ids.size() == 0) return;
MatrixXd H_x = MatrixXd::Zero(jacobian_row_size,
21+6*state_server.cam_states.size());
VectorXd r = VectorXd::Zero(jacobian_row_size);
int stack_cntr = 0;
// Process the features which lose track.
for (const auto& feature_id : processed_feature_ids) {
auto& feature = map_server[feature_id];
vector<StateIDType> cam_state_ids(0);
for (const auto& measurement : feature.observations)
cam_state_ids.push_back(measurement.first);
MatrixXd H_xj;
VectorXd r_j;
featureJacobian(feature.id, cam_state_ids, H_xj, r_j);
if (gatingTest(H_xj, r_j, cam_state_ids.size()-1)) {
H_x.block(stack_cntr, 0, H_xj.rows(), H_xj.cols()) = H_xj;
r.segment(stack_cntr, r_j.rows()) = r_j;
stack_cntr += H_xj.rows();
}
// Put an upper bound on the row size of measurement Jacobian,
// which helps guarantee the executation time.
if (stack_cntr > 1500) break;
}
H_x.conservativeResize(stack_cntr, H_x.cols());
r.conservativeResize(stack_cntr);
// Perform the measurement update step.
measurementUpdate(H_x, r);
// Remove all processed features from the map.
for (const auto& feature_id : processed_feature_ids)
map_server.erase(feature_id);
return;
}
void MsckfVio::findRedundantCamStates(
vector<StateIDType>& rm_cam_state_ids) {
// Move the iterator to the key position.
auto key_cam_state_iter = state_server.cam_states.end();
for (int i = 0; i < 4; ++i)
--key_cam_state_iter;
auto cam_state_iter = key_cam_state_iter;
++cam_state_iter;
auto first_cam_state_iter = state_server.cam_states.begin();
// Pose of the key camera state.
const Vector3d key_position =
key_cam_state_iter->second.position;
const Matrix3d key_rotation = quaternionToRotation(
key_cam_state_iter->second.orientation);
// Mark the camera states to be removed based on the
// motion between states.
for (int i = 0; i < 2; ++i) {
const Vector3d position =
cam_state_iter->second.position;
const Matrix3d rotation = quaternionToRotation(
cam_state_iter->second.orientation);
double distance = (position-key_position).norm();
double angle = AngleAxisd(
rotation*key_rotation.transpose()).angle();
//if (angle < 0.1745 && distance < 0.2 && tracking_rate > 0.5) {
if (angle < 0.2618 && distance < 0.4 && tracking_rate > 0.5) {
rm_cam_state_ids.push_back(cam_state_iter->first);
++cam_state_iter;
} else {
rm_cam_state_ids.push_back(first_cam_state_iter->first);
++first_cam_state_iter;
}
}
// Sort the elements in the output vector.
sort(rm_cam_state_ids.begin(), rm_cam_state_ids.end());
return;
}
void MsckfVio::pruneCamStateBuffer() {
if (state_server.cam_states.size() < max_cam_state_size)
return;
// Find two camera states to be removed.
vector<StateIDType> rm_cam_state_ids(0);
findRedundantCamStates(rm_cam_state_ids);
// Find the size of the Jacobian matrix.
int jacobian_row_size = 0;
for (auto& item : map_server) {
auto& feature = item.second;
// Check how many camera states to be removed are associated
// with this feature.
vector<StateIDType> involved_cam_state_ids(0);
for (const auto& cam_id : rm_cam_state_ids) {
if (feature.observations.find(cam_id) !=
feature.observations.end())
involved_cam_state_ids.push_back(cam_id);
}
if (involved_cam_state_ids.size() == 0) continue;
if (involved_cam_state_ids.size() == 1) {
feature.observations.erase(involved_cam_state_ids[0]);
continue;
}
if (!feature.is_initialized) {
// Check if the feature can be initialize.
if (!feature.checkMotion(state_server.cam_states)) {
// If the feature cannot be initialized, just remove
// the observations associated with the camera states
// to be removed.
for (const auto& cam_id : involved_cam_state_ids)
feature.observations.erase(cam_id);
continue;
} else {
if(!feature.initializePosition(state_server.cam_states)) {
for (const auto& cam_id : involved_cam_state_ids)
feature.observations.erase(cam_id);
continue;
}
}
}
jacobian_row_size += 4*involved_cam_state_ids.size() - 3;
}
//cout << "jacobian row #: " << jacobian_row_size << endl;
// Compute the Jacobian and residual.
MatrixXd H_x = MatrixXd::Zero(jacobian_row_size,
21+6*state_server.cam_states.size());
VectorXd r = VectorXd::Zero(jacobian_row_size);
int stack_cntr = 0;
for (auto& item : map_server) {
auto& feature = item.second;
// Check how many camera states to be removed are associated
// with this feature.
vector<StateIDType> involved_cam_state_ids(0);
for (const auto& cam_id : rm_cam_state_ids) {
if (feature.observations.find(cam_id) !=
feature.observations.end())
involved_cam_state_ids.push_back(cam_id);
}
if (involved_cam_state_ids.size() == 0) continue;
MatrixXd H_xj;
VectorXd r_j;
featureJacobian(feature.id, involved_cam_state_ids, H_xj, r_j);
if (gatingTest(H_xj, r_j, involved_cam_state_ids.size())) {
H_x.block(stack_cntr, 0, H_xj.rows(), H_xj.cols()) = H_xj;
r.segment(stack_cntr, r_j.rows()) = r_j;
stack_cntr += H_xj.rows();
}
for (const auto& cam_id : involved_cam_state_ids)
feature.observations.erase(cam_id);
}
H_x.conservativeResize(stack_cntr, H_x.cols());
r.conservativeResize(stack_cntr);
// Perform measurement update.
measurementUpdate(H_x, r);
for (const auto& cam_id : rm_cam_state_ids) {
int cam_sequence = std::distance(state_server.cam_states.begin(),
state_server.cam_states.find(cam_id));
int cam_state_start = 21 + 6*cam_sequence;
int cam_state_end = cam_state_start + 6;
// Remove the corresponding rows and columns in the state
// covariance matrix.
if (cam_state_end < state_server.state_cov.rows()) {
state_server.state_cov.block(cam_state_start, 0,
state_server.state_cov.rows()-cam_state_end,
state_server.state_cov.cols()) =
state_server.state_cov.block(cam_state_end, 0,
state_server.state_cov.rows()-cam_state_end,
state_server.state_cov.cols());
state_server.state_cov.block(0, cam_state_start,
state_server.state_cov.rows(),
state_server.state_cov.cols()-cam_state_end) =
state_server.state_cov.block(0, cam_state_end,
state_server.state_cov.rows(),
state_server.state_cov.cols()-cam_state_end);
state_server.state_cov.conservativeResize(
state_server.state_cov.rows()-6, state_server.state_cov.cols()-6);
} else {
state_server.state_cov.conservativeResize(
state_server.state_cov.rows()-6, state_server.state_cov.cols()-6);
}
// Remove this camera state in the state vector.
state_server.cam_states.erase(cam_id);
}
return;
}
void MsckfVio::onlineReset() {
// Never perform online reset if position std threshold
// is non-positive.
if (position_std_threshold <= 0) return;
static long long int online_reset_counter = 0;
// Check the uncertainty of positions to determine if
// the system can be reset.
double position_x_std = std::sqrt(state_server.state_cov(12, 12));
double position_y_std = std::sqrt(state_server.state_cov(13, 13));
double position_z_std = std::sqrt(state_server.state_cov(14, 14));
if (position_x_std < position_std_threshold &&
position_y_std < position_std_threshold &&
position_z_std < position_std_threshold) return;
ROS_WARN("Start %lld online reset procedure...",
++online_reset_counter);
ROS_INFO("Stardard deviation in xyz: %f, %f, %f",
position_x_std, position_y_std, position_z_std);
// Remove all existing camera states.
state_server.cam_states.clear();
// Clear all exsiting features in the map.
map_server.clear();
// Reset the state covariance.
double gyro_bias_cov, acc_bias_cov, velocity_cov;
nh.param<double>("initial_covariance/velocity",
velocity_cov, 0.25);
nh.param<double>("initial_covariance/gyro_bias",
gyro_bias_cov, 1e-4);
nh.param<double>("initial_covariance/acc_bias",
acc_bias_cov, 1e-2);
double extrinsic_rotation_cov, extrinsic_translation_cov;
nh.param<double>("initial_covariance/extrinsic_rotation_cov",
extrinsic_rotation_cov, 3.0462e-4);
nh.param<double>("initial_covariance/extrinsic_translation_cov",
extrinsic_translation_cov, 1e-4);
state_server.state_cov = 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;
ROS_WARN("%lld online reset complete...", online_reset_counter);
return;
}
void MsckfVio::publish(const ros::Time& time) {
// Convert the IMU frame to the body frame.
const IMUState& imu_state = state_server.imu_state;
Eigen::Isometry3d T_i_w = Eigen::Isometry3d::Identity();
T_i_w.linear() = quaternionToRotation(
imu_state.orientation).transpose();
T_i_w.translation() = imu_state.position;
Eigen::Isometry3d T_b_w = IMUState::T_imu_body * T_i_w *
IMUState::T_imu_body.inverse();
Eigen::Vector3d body_velocity =
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.
Matrix3d P_oo = state_server.state_cov.block<3, 3>(0, 0);
Matrix3d P_op = state_server.state_cov.block<3, 3>(0, 12);
Matrix3d P_po = state_server.state_cov.block<3, 3>(12, 0);
Matrix3d P_pp = state_server.state_cov.block<3, 3>(12, 12);
Matrix<double, 6, 6> P_imu_pose = Matrix<double, 6, 6>::Zero();
P_imu_pose << P_pp, P_po, P_op, P_oo;
Matrix<double, 6, 6> H_pose = Matrix<double, 6, 6>::Zero();
H_pose.block<3, 3>(0, 0) = IMUState::T_imu_body.linear();
H_pose.block<3, 3>(3, 3) = IMUState::T_imu_body.linear();
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.
Matrix3d P_imu_vel = state_server.state_cov.block<3, 3>(6, 6);
Matrix3d H_vel = IMUState::T_imu_body.linear();
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;
}
} // namespace msckf_vio
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