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ros_filter.cpp
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/*
* Copyright (c) 2014, 2015, 2016 Charles River Analytics, Inc.
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* 1. Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* 2. 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.
* 3. Neither the name of the copyright holder 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 "robot_localization/ros_filter.hpp"
#include <algorithm>
#include <chrono>
#include <cmath>
#include <iomanip>
#include <functional>
#include <limits>
#include <memory>
#include <sstream>
#include <stdexcept>
#include <string>
#include <utility>
#include <vector>
#include "angles/angles.h"
#include "diagnostic_msgs/msg/diagnostic_status.hpp"
#include "diagnostic_updater/diagnostic_status_wrapper.hpp"
#include "diagnostic_updater/diagnostic_updater.hpp"
#include "diagnostic_updater/publisher.hpp"
#include "Eigen/Dense"
#include "geometry_msgs/msg/accel_with_covariance_stamped.hpp"
#include "geometry_msgs/msg/pose_with_covariance_stamped.hpp"
#include "geometry_msgs/msg/transform_stamped.hpp"
#include "geometry_msgs/msg/twist.hpp"
#include "geometry_msgs/msg/twist_stamped.hpp"
#include "nav_msgs/msg/odometry.hpp"
#include "rclcpp/qos.hpp"
#include "rclcpp/rclcpp.hpp"
#include "robot_localization/ekf.hpp"
#include "robot_localization/filter_common.hpp"
#include "robot_localization/filter_state.hpp"
#include "robot_localization/filter_utilities.hpp"
#include "robot_localization/ros_filter_utilities.hpp"
#include "robot_localization/srv/set_pose.hpp"
#include "robot_localization/srv/toggle_filter_processing.hpp"
#include "robot_localization/ukf.hpp"
#include "sensor_msgs/msg/imu.hpp"
#include "std_srvs/srv/empty.hpp"
#include "tf2/LinearMath/Matrix3x3.h"
#include "tf2/LinearMath/Quaternion.h"
#include "tf2/LinearMath/Transform.h"
#include "tf2/LinearMath/Vector3.h"
#include "tf2_geometry_msgs/tf2_geometry_msgs.hpp"
#include "tf2_ros/buffer.h"
#include "tf2_ros/transform_broadcaster.h"
#include "tf2_ros/transform_listener.h"
namespace robot_localization
{
using namespace std::chrono_literals;
template<typename T>
RosFilter<T>::RosFilter(const rclcpp::NodeOptions & options)
: Node(options.arguments()[0], options),
print_diagnostics_(true),
publish_acceleration_(false),
publish_transform_(true),
reset_on_time_jump_(false),
smooth_lagged_data_(false),
toggled_on_(true),
two_d_mode_(false),
use_control_(false),
disabled_at_startup_(false),
enabled_(false),
permit_corrected_publication_(false),
dynamic_diag_error_level_(diagnostic_msgs::msg::DiagnosticStatus::OK),
static_diag_error_level_(diagnostic_msgs::msg::DiagnosticStatus::OK),
frequency_(30.0),
gravitational_acceleration_(9.80665),
history_length_(0ns),
sensor_timeout_(0ns),
latest_control_(),
process_noise_covariance_(STATE_SIZE, STATE_SIZE),
initial_estimate_error_covariance_(STATE_SIZE, STATE_SIZE),
last_diag_time_(0, 0, RCL_ROS_TIME),
last_published_stamp_(0, 0, RCL_ROS_TIME),
predict_to_current_time_(false),
last_set_pose_time_(0, 0, RCL_ROS_TIME),
latest_control_time_(0, 0, RCL_ROS_TIME),
tf_timeout_(0ns),
tf_time_offset_(0ns)
{
tf_buffer_ = std::make_unique<tf2_ros::Buffer>(this->get_clock());
tf_listener_ = std::make_unique<tf2_ros::TransformListener>(*tf_buffer_);
state_variable_names_.push_back("X");
state_variable_names_.push_back("Y");
state_variable_names_.push_back("Z");
state_variable_names_.push_back("ROLL");
state_variable_names_.push_back("PITCH");
state_variable_names_.push_back("YAW");
state_variable_names_.push_back("X_VELOCITY");
state_variable_names_.push_back("Y_VELOCITY");
state_variable_names_.push_back("Z_VELOCITY");
state_variable_names_.push_back("ROLL_VELOCITY");
state_variable_names_.push_back("PITCH_VELOCITY");
state_variable_names_.push_back("YAW_VELOCITY");
state_variable_names_.push_back("X_ACCELERATION");
state_variable_names_.push_back("Y_ACCELERATION");
state_variable_names_.push_back("Z_ACCELERATION");
}
template<typename T>
RosFilter<T>::~RosFilter()
{
topic_subs_.clear();
timer_.reset();
set_pose_sub_.reset();
control_sub_.reset();
tf_listener_.reset();
tf_buffer_.reset();
diagnostic_updater_.reset();
world_transform_broadcaster_.reset();
set_pose_service_.reset();
freq_diag_.reset();
accel_pub_.reset();
position_pub_.reset();
}
template<typename T>
void RosFilter<T>::reset()
{
// Get rid of any initial poses (pretend we've never had a measurement)
initial_measurements_.clear();
previous_measurements_.clear();
previous_measurement_covariances_.clear();
clearMeasurementQueue();
filter_state_history_.clear();
measurement_history_.clear();
// Also set the last set pose time, so we ignore all messages
// that occur before it
last_set_pose_time_ = rclcpp::Time(0, 0, RCL_ROS_TIME);
last_diag_time_ = rclcpp::Time(0, 0, RCL_ROS_TIME);
latest_control_time_ = rclcpp::Time(0, 0, RCL_ROS_TIME);
last_published_stamp_ = rclcpp::Time(0, 0, RCL_ROS_TIME);
// clear tf buffer to avoid TF_OLD_DATA errors
tf_buffer_->clear();
// clear last message timestamp, so older messages will be accepted
last_message_times_.clear();
// reset filter to uninitialized state
filter_.reset();
// Restore filter parameters that we got from the ROS parameter server
filter_.setSensorTimeout(sensor_timeout_);
filter_.setProcessNoiseCovariance(process_noise_covariance_);
filter_.setEstimateErrorCovariance(initial_estimate_error_covariance_);
}
template<typename T>
void RosFilter<T>::resetSrvCallback(
const std::shared_ptr<rmw_request_id_t>,
const std::shared_ptr<std_srvs::srv::Empty::Request>,
const std::shared_ptr<std_srvs::srv::Empty::Response>)
{
RCLCPP_INFO(
get_logger(),
"Received a request to reset filter.");
reset();
}
template<typename T>
void RosFilter<T>::toggleFilterProcessingCallback(
const std::shared_ptr<rmw_request_id_t>/*request_header*/,
const std::shared_ptr<
robot_localization::srv::ToggleFilterProcessing::Request> req,
const std::shared_ptr<
robot_localization::srv::ToggleFilterProcessing::Response> resp)
{
if (req->on == toggled_on_) {
RCLCPP_WARN(
get_logger(),
"Service was called to toggle filter processing but state was already as "
"requested.");
resp->status = false;
} else {
RCLCPP_INFO(
get_logger(),
"Toggling filter measurement filtering to %s.", req->on ? "On" : "Off");
toggled_on_ = req->on;
resp->status = true;
}
}
// @todo: Replace with AccelWithCovarianceStamped
template<typename T>
void RosFilter<T>::accelerationCallback(
const sensor_msgs::msg::Imu::SharedPtr msg,
const CallbackData & callback_data,
const std::string & target_frame)
{
// If we've just reset the filter, then we want to ignore any messages
// that arrive with an older timestamp
if (last_set_pose_time_ >= msg->header.stamp) {
return;
}
const std::string & topic_name = callback_data.topic_name_;
RF_DEBUG(
"------ RosFilter<T>::accelerationCallback (" << topic_name <<
") ------\n")
// "Twist message:\n" << *msg);
if (last_message_times_.count(topic_name) == 0) {
last_message_times_.insert(
std::pair<std::string, rclcpp::Time>(topic_name, msg->header.stamp));
}
// Make sure this message is newer than the last one
if (last_message_times_[topic_name] <= msg->header.stamp) {
RF_DEBUG("Update vector for " << topic_name << " is:\n" << topic_name);
Eigen::VectorXd measurement(STATE_SIZE);
Eigen::MatrixXd measurement_covariance(STATE_SIZE, STATE_SIZE);
measurement.setZero();
measurement_covariance.setZero();
// Make sure we're actually updating at least one of these variables
std::vector<bool> update_vector_corrected = callback_data.update_vector_;
// Prepare the twist data for inclusion in the filter
if (prepareAcceleration(
msg, topic_name, target_frame, callback_data.relative_,
update_vector_corrected, measurement,
measurement_covariance))
{
// Store the measurement. Add an "acceleration" suffix so we know what
// kind of measurement we're dealing with when we debug the core filter
// logic.
enqueueMeasurement(
topic_name, measurement, measurement_covariance,
update_vector_corrected,
callback_data.rejection_threshold_, msg->header.stamp);
RF_DEBUG(
"Enqueued new measurement for " << topic_name <<
"_acceleration\n");
} else {
RF_DEBUG(
"Did *not* enqueue measurement for " << topic_name <<
"_acceleration\n");
}
last_message_times_[topic_name] = msg->header.stamp;
RF_DEBUG(
"Last message time for " <<
topic_name << " is now " <<
filter_utilities::toSec(last_message_times_[topic_name]) <<
"\n");
} else {
// else if (reset_on_time_jump_ && rclcpp::Time::isSimTime())
//{
// reset();
//}
std::stringstream stream;
stream << "The " << topic_name << " message has a timestamp before that of "
"the previous message received," << " this message will be ignored. This may"
" indicate a bad timestamp. (message time: " << msg->header.stamp.nanosec <<
")";
addDiagnostic(
diagnostic_msgs::msg::DiagnosticStatus::WARN, topic_name +
"_timestamp", stream.str(), false);
RF_DEBUG(
"Message is too old. Last message time for " <<
topic_name << " is " <<
filter_utilities::toSec(last_message_times_[topic_name]) <<
", current message time is " <<
filter_utilities::toSec(msg->header.stamp) << ".\n");
}
RF_DEBUG(
"\n----- /RosFilter<T>::accelerationCallback (" << topic_name <<
") ------\n");
}
template<typename T>
void RosFilter<T>::controlCallback(
const geometry_msgs::msg::Twist::SharedPtr msg)
{
geometry_msgs::msg::TwistStamped::SharedPtr twist_stamped_ptr =
std::make_shared<geometry_msgs::msg::TwistStamped>();
twist_stamped_ptr->twist = *msg;
twist_stamped_ptr->header.frame_id = base_link_frame_id_;
twist_stamped_ptr->header.stamp = this->now();
controlStampedCallback(twist_stamped_ptr);
}
template<typename T>
void RosFilter<T>::controlStampedCallback(
const geometry_msgs::msg::TwistStamped::SharedPtr msg)
{
if (msg->header.frame_id == base_link_frame_id_ ||
msg->header.frame_id == "")
{
latest_control_(ControlMemberVx) = msg->twist.linear.x;
latest_control_(ControlMemberVy) = msg->twist.linear.y;
latest_control_(ControlMemberVz) = msg->twist.linear.z;
latest_control_(ControlMemberVroll) = msg->twist.angular.x;
latest_control_(ControlMemberVpitch) = msg->twist.angular.y;
latest_control_(ControlMemberVyaw) = msg->twist.angular.z;
latest_control_time_ = msg->header.stamp;
// Update the filter with this control term
filter_.setControl(latest_control_, msg->header.stamp);
} else {
RCLCPP_WARN_STREAM_THROTTLE(
get_logger(), *get_clock(), 5.0, "Commanded velocities "
" must be given in the robot's body frame (" << base_link_frame_id_ <<
"). Message frame was " << msg->header.frame_id);
}
}
template<typename T>
void RosFilter<T>::enqueueMeasurement(
const std::string & topic_name, const Eigen::VectorXd & measurement,
const Eigen::MatrixXd & measurement_covariance,
const std::vector<bool> & update_vector, const double mahalanobis_thresh,
const rclcpp::Time & time)
{
MeasurementPtr meas = MeasurementPtr(new Measurement());
meas->topic_name_ = topic_name;
meas->measurement_ = measurement;
meas->covariance_ = measurement_covariance;
meas->update_vector_ = update_vector;
meas->time_ = time;
meas->mahalanobis_thresh_ = mahalanobis_thresh;
meas->latest_control_ = latest_control_;
meas->latest_control_time_ = latest_control_time_;
measurement_queue_.push(meas);
}
template<typename T>
void RosFilter<T>::forceTwoD(
Eigen::VectorXd & measurement,
Eigen::MatrixXd & measurement_covariance,
std::vector<bool> & update_vector)
{
measurement(StateMemberZ) = 0.0;
measurement(StateMemberRoll) = 0.0;
measurement(StateMemberPitch) = 0.0;
measurement(StateMemberVz) = 0.0;
measurement(StateMemberVroll) = 0.0;
measurement(StateMemberVpitch) = 0.0;
measurement(StateMemberAz) = 0.0;
measurement_covariance(StateMemberZ, StateMemberZ) = 1e-6;
measurement_covariance(StateMemberRoll, StateMemberRoll) = 1e-6;
measurement_covariance(StateMemberPitch, StateMemberPitch) = 1e-6;
measurement_covariance(StateMemberVz, StateMemberVz) = 1e-6;
measurement_covariance(StateMemberVroll, StateMemberVroll) = 1e-6;
measurement_covariance(StateMemberVpitch, StateMemberVpitch) = 1e-6;
measurement_covariance(StateMemberAz, StateMemberAz) = 1e-6;
update_vector[StateMemberZ] = 1;
update_vector[StateMemberRoll] = 1;
update_vector[StateMemberPitch] = 1;
update_vector[StateMemberVz] = 1;
update_vector[StateMemberVroll] = 1;
update_vector[StateMemberVpitch] = 1;
update_vector[StateMemberAz] = 1;
}
template<typename T>
bool RosFilter<T>::getFilteredOdometryMessage(nav_msgs::msg::Odometry * message)
{
// If the filter has received a measurement at some point...
if (filter_.getInitializedStatus()) {
// Grab our current state and covariance estimates
const Eigen::VectorXd & state = filter_.getState();
const Eigen::MatrixXd & estimate_error_covariance =
filter_.getEstimateErrorCovariance();
// Convert from roll, pitch, and yaw back to quaternion for
// orientation values
tf2::Quaternion quat;
quat.setRPY(
state(StateMemberRoll), state(StateMemberPitch),
state(StateMemberYaw));
// Fill out the message
message->pose.pose.position.x = state(StateMemberX);
message->pose.pose.position.y = state(StateMemberY);
message->pose.pose.position.z = state(StateMemberZ);
message->pose.pose.orientation.x = quat.x();
message->pose.pose.orientation.y = quat.y();
message->pose.pose.orientation.z = quat.z();
message->pose.pose.orientation.w = quat.w();
message->twist.twist.linear.x = state(StateMemberVx);
message->twist.twist.linear.y = state(StateMemberVy);
message->twist.twist.linear.z = state(StateMemberVz);
message->twist.twist.angular.x = state(StateMemberVroll);
message->twist.twist.angular.y = state(StateMemberVpitch);
message->twist.twist.angular.z = state(StateMemberVyaw);
// Our covariance matrix layout doesn't quite match
for (size_t i = 0; i < POSE_SIZE; i++) {
for (size_t j = 0; j < POSE_SIZE; j++) {
message->pose.covariance[POSE_SIZE * i + j] =
estimate_error_covariance(i, j);
}
}
// POSE_SIZE and TWIST_SIZE are currently the same size, but we can spare a
// few cycles to be meticulous and not index a twist covariance array on the
// size of a pose covariance array
for (size_t i = 0; i < TWIST_SIZE; i++) {
for (size_t j = 0; j < TWIST_SIZE; j++) {
message->twist.covariance[TWIST_SIZE * i + j] =
estimate_error_covariance(
i + POSITION_V_OFFSET,
j + POSITION_V_OFFSET);
}
}
message->header.stamp = filter_.getLastMeasurementTime();
message->header.frame_id = world_frame_id_;
message->child_frame_id = base_link_output_frame_id_;
}
return filter_.getInitializedStatus();
}
template<typename T>
bool RosFilter<T>::getFilteredAccelMessage(
geometry_msgs::msg::AccelWithCovarianceStamped * message)
{
// If the filter has received a measurement at some point...
if (filter_.getInitializedStatus()) {
// Grab our current state and covariance estimates
const Eigen::VectorXd & state = filter_.getState();
const Eigen::MatrixXd & estimate_error_covariance =
filter_.getEstimateErrorCovariance();
//! Fill out the accel_msg
message->accel.accel.linear.x = state(StateMemberAx);
message->accel.accel.linear.y = state(StateMemberAy);
message->accel.accel.linear.z = state(StateMemberAz);
message->accel.accel.angular.x = angular_acceleration_.x();
message->accel.accel.angular.y = angular_acceleration_.y();
message->accel.accel.angular.z = angular_acceleration_.z();
// Fill the covariance (only the left-upper matrix since we are not
// estimating the rotational accelerations arround the axes
for (size_t i = 0; i < ACCELERATION_SIZE; i++) {
for (size_t j = 0; j < ACCELERATION_SIZE; j++) {
// We use the POSE_SIZE since the accel cov matrix of ROS is 6x6
message->accel.covariance[POSE_SIZE * i + j] = estimate_error_covariance(
i + POSITION_A_OFFSET, j + POSITION_A_OFFSET);
}
}
for (size_t i = ACCELERATION_SIZE; i < POSE_SIZE; i++) {
for (size_t j = ACCELERATION_SIZE; j < POSE_SIZE; j++) {
// fill out the angular portion. We assume the linear and angular portions are independent.
message->accel.covariance[POSE_SIZE * i + j] =
angular_acceleration_cov_(i - ACCELERATION_SIZE, j - ACCELERATION_SIZE);
}
}
// Fill header information
message->header.stamp = rclcpp::Time(filter_.getLastMeasurementTime());
message->header.frame_id = base_link_output_frame_id_;
}
return filter_.getInitializedStatus();
}
template<typename T>
void RosFilter<T>::imuCallback(
const sensor_msgs::msg::Imu::SharedPtr msg,
const std::string & topic_name,
const CallbackData & pose_callback_data,
const CallbackData & twist_callback_data,
const CallbackData & accel_callback_data)
{
RF_DEBUG(
"------ RosFilter<T>::imuCallback (" <<
topic_name << ") ------\n") // << "IMU message:\n" << *msg);
// If we've just reset the filter, then we want to ignore any messages
// that arrive with an older timestamp
if (last_set_pose_time_ >= msg->header.stamp) {
std::stringstream stream;
stream << "The " << topic_name << " message has a timestamp equal to or"
" before the last filter reset, " << "this message will be ignored. This may"
"indicate an empty or bad timestamp. (message time: " << msg->header.stamp.nanosec <<
")";
addDiagnostic(
diagnostic_msgs::msg::DiagnosticStatus::WARN,
topic_name + "_timestamp", stream.str(), false);
RF_DEBUG(
"Received message that preceded the most recent pose reset. "
"Ignoring...");
return;
}
// As with the odometry message, we can separate out the pose- and
// twist-related variables in the IMU message and pass them to the pose and
// twist callbacks (filters)
if (pose_callback_data.update_sum_ > 0) {
// Per the IMU message specification, if the IMU does not provide
// orientation, then its first covariance value should be set to -1, and we
// should ignore that portion of the message. robot_localization allows
// users to explicitly ignore data using its parameters, but we should also
// be compliant with message specs.
if (std::abs(msg->orientation_covariance[0] + 1) < 1e-9) {
RF_DEBUG(
"Received IMU message with -1 as its first covariance value for "
"orientation. "
"Ignoring orientation...");
} else {
// Extract the pose (orientation) data, pass it to its filter
geometry_msgs::msg::PoseWithCovarianceStamped::SharedPtr pos_ptr =
std::make_shared<geometry_msgs::msg::PoseWithCovarianceStamped>();
pos_ptr->header = msg->header;
pos_ptr->pose.pose.orientation = msg->orientation;
// Copy the covariance for roll, pitch, and yaw
for (size_t i = 0; i < ORIENTATION_SIZE; i++) {
for (size_t j = 0; j < ORIENTATION_SIZE; j++) {
pos_ptr->pose.covariance[POSE_SIZE * (i + ORIENTATION_SIZE) +
(j + ORIENTATION_SIZE)] =
msg->orientation_covariance[ORIENTATION_SIZE * i + j];
}
}
// IMU data gets handled a bit differently, since the message is ambiguous
// and has only a single frame_id, even though the data in it is reported
// in two different frames. As we assume users will specify a base_link to
// imu transform, we make the target and child frame base_link_frame_id_ and
// tell the poseCallback that it is working with IMU data. This will cause
// it to apply different logic to the data.
poseCallback(
pos_ptr, pose_callback_data, base_link_frame_id_,
base_link_frame_id_, true);
}
}
if (twist_callback_data.update_sum_ > 0) {
// Ignore rotational velocity if the first covariance value is -1
if (std::abs(msg->angular_velocity_covariance[0] + 1) < 1e-9) {
RF_DEBUG(
"Received IMU message with -1 as its first covariance value for "
"angular "
"velocity. Ignoring angular velocity...");
} else {
// Repeat for velocity
geometry_msgs::msg::TwistWithCovarianceStamped::SharedPtr twist_ptr =
std::make_shared<geometry_msgs::msg::TwistWithCovarianceStamped>();
twist_ptr->header = msg->header;
twist_ptr->twist.twist.angular = msg->angular_velocity;
// Copy the covariance
for (size_t i = 0; i < ORIENTATION_SIZE; i++) {
for (size_t j = 0; j < ORIENTATION_SIZE; j++) {
twist_ptr->twist.covariance[TWIST_SIZE * (i + ORIENTATION_SIZE) +
(j + ORIENTATION_SIZE)] =
msg->angular_velocity_covariance[ORIENTATION_SIZE * i + j];
}
}
twistCallback(twist_ptr, twist_callback_data, base_link_frame_id_);
}
}
if (accel_callback_data.update_sum_ > 0) {
// Ignore linear acceleration if the first covariance value is -1
if (std::abs(msg->linear_acceleration_covariance[0] + 1) < 1e-9) {
RF_DEBUG(
"Received IMU message with -1 as its first covariance value for "
"linear "
"acceleration. Ignoring linear acceleration...");
} else {
// Pass the message on
accelerationCallback(msg, accel_callback_data, base_link_frame_id_);
}
}
RF_DEBUG("\n----- /RosFilter<T>::imuCallback (" << topic_name << ") ------\n");
}
template<typename T>
void RosFilter<T>::integrateMeasurements(const rclcpp::Time & current_time)
{
RF_DEBUG(
"------ RosFilter<T>::integrateMeasurements ------\n\n"
"Integration time is " <<
std::setprecision(20) << filter_utilities::toSec(current_time) <<
"\n" <<
measurement_queue_.size() << " measurements in queue.\n");
bool predict_to_current_time = predict_to_current_time_;
// If we have any measurements in the queue, process them
if (!measurement_queue_.empty()) {
// Check if the first measurement we're going to process is older than the
// filter's last measurement. This means we have received an out-of-sequence
// message (one with an old timestamp), and we need to revert both the
// filter state and measurement queue to the first state that preceded the
// time stamp of our first measurement.
const MeasurementPtr & first_measurement = measurement_queue_.top();
int restored_measurement_count = 0;
if (smooth_lagged_data_ &&
first_measurement->time_ < filter_.getLastMeasurementTime())
{
RF_DEBUG(
"Received a measurement that was " <<
filter_utilities::toSec(
filter_.getLastMeasurementTime() -
first_measurement->time_) <<
" seconds in the past. Reverting filter state and "
"measurement queue...");
int original_count = static_cast<int>(measurement_queue_.size());
const rclcpp::Time first_measurement_time = first_measurement->time_;
const std::string first_measurement_topic =
first_measurement->topic_name_;
// revertTo may invalidate first_measurement
if (!revertTo(first_measurement_time - rclcpp::Duration(1ns))) {
RF_DEBUG(
"ERROR: history interval is too small to revert to time " <<
filter_utilities::toSec(first_measurement_time) << "\n");
// ROS_WARN_STREAM_DELAYED_THROTTLE(history_length_,
// "Received old measurement for topic " << first_measurement_topic <<
// ", but history interval is insufficiently sized. "
// "Measurement time is " << std::setprecision(20) <<
// first_measurement_time <<
// ", current time is " << current_time <<
// ", history length is " << history_length_ << ".");
restored_measurement_count = 0;
}
restored_measurement_count =
static_cast<int>(measurement_queue_.size()) - original_count;
}
while (!measurement_queue_.empty() && rclcpp::ok()) {
MeasurementPtr measurement = measurement_queue_.top();
// If we've reached a measurement that has a time later than now, it
// should wait until a future iteration. Since measurements are stored in
// a priority queue, all remaining measurements will be in the future.
if (current_time < measurement->time_) {
break;
}
measurement_queue_.pop();
// When we receive control messages, we call this directly in the control
// callback. However, we also associate a control with each sensor message
// so that we can support lagged smoothing. As we cannot guarantee that
// the new control callback will fire before a new measurement, we should
// only perform this operation if we are processing messages from the
// history. Otherwise, we may get a new measurement, store the "old"
// latest control, then receive a control, call setControl, and then
// overwrite that value with this one (i.e., with the "old" control we
// associated with the measurement).
if (use_control_ && restored_measurement_count > 0) {
filter_.setControl(
measurement->latest_control_,
measurement->latest_control_time_);
restored_measurement_count--;
}
// This will call predict and, if necessary, correct
filter_.processMeasurement(*(measurement.get()));
// Store old states and measurements if we're smoothing
if (smooth_lagged_data_) {
// Invariant still holds: measurementHistoryDeque_.back().time_ <
// measurement_queue_.top().time_
measurement_history_.push_back(measurement);
// We should only save the filter state once per unique timstamp
if (measurement_queue_.empty() ||
measurement_queue_.top()->time_ !=
filter_.getLastMeasurementTime())
{
saveFilterState(filter_);
}
}
}
} else if (filter_.getInitializedStatus()) {
// In the event that we don't get any measurements for a long time,
// we still need to continue to estimate our state. Therefore, we
// should project the state forward here.
rclcpp::Duration last_update_delta =
current_time - filter_.getLastMeasurementTime();
// If we get a large delta, then continuously predict until
if (last_update_delta >= filter_.getSensorTimeout()) {
predict_to_current_time = true;
RF_DEBUG(
"Sensor timeout! Last measurement time was " <<
filter_utilities::toSec(filter_.getLastMeasurementTime()) <<
", current time is " << filter_utilities::toSec(current_time) <<
", delta is " << filter_utilities::toSec(last_update_delta) <<
"\n");
}
} else {
RF_DEBUG("Filter not yet initialized.\n");
}
if (filter_.getInitializedStatus() && predict_to_current_time) {
rclcpp::Duration last_update_delta =
current_time - filter_.getLastMeasurementTime();
filter_.validateDelta(last_update_delta);
filter_.predict(current_time, last_update_delta);
// Update the last measurement time and last update time
filter_.setLastMeasurementTime(
filter_.getLastMeasurementTime() +
last_update_delta);
}
RF_DEBUG("\n----- /RosFilter<T>::integrateMeasurements ------\n");
}
template<typename T>
void RosFilter<T>::differentiateMeasurements(const rclcpp::Time & current_time)
{
if (filter_.getInitializedStatus()) {
const double time_now = filter_utilities::toSec(current_time);
const double dt = time_now - last_diff_time_;
const Eigen::VectorXd & state = filter_.getState();
tf2::Vector3 new_state_twist_rot(
state(StateMemberVroll),
state(StateMemberVpitch),
state(StateMemberVyaw));
angular_acceleration_ = (new_state_twist_rot - last_state_twist_rot_) / dt;
const Eigen::MatrixXd & cov = filter_.getEstimateErrorCovariance();
for (size_t i = 0; i < ORIENTATION_SIZE; i++) {
for (size_t j = 0; j < ORIENTATION_SIZE; j++) {
angular_acceleration_cov_(i, j) =
cov(i + ORIENTATION_V_OFFSET, j + ORIENTATION_V_OFFSET) * 2. /
( dt * dt );
}
}
last_state_twist_rot_ = new_state_twist_rot;
last_diff_time_ = time_now;
}
}
template<typename T>
void RosFilter<T>::loadParams()
{
/* For diagnostic purposes, collect information about how many different
* sources are measuring each absolute pose variable and do not have
* differential integration enabled.
*/
std::map<StateMembers, int> abs_pose_var_counts;
abs_pose_var_counts[StateMemberX] = 0;
abs_pose_var_counts[StateMemberY] = 0;
abs_pose_var_counts[StateMemberZ] = 0;
abs_pose_var_counts[StateMemberRoll] = 0;
abs_pose_var_counts[StateMemberPitch] = 0;
abs_pose_var_counts[StateMemberYaw] = 0;
// Same for twist variables
std::map<StateMembers, int> twist_var_counts;
twist_var_counts[StateMemberVx] = 0;
twist_var_counts[StateMemberVy] = 0;
twist_var_counts[StateMemberVz] = 0;
twist_var_counts[StateMemberVroll] = 0;
twist_var_counts[StateMemberVpitch] = 0;
twist_var_counts[StateMemberVyaw] = 0;
// Determine if we'll be printing diagnostic information
print_diagnostics_ = this->declare_parameter("print_diagnostics", false);
// Check for custom gravitational acceleration value
gravitational_acceleration_ = this->declare_parameter(
"gravitational_acceleration",
gravitational_acceleration_);
// Grab the debug param. If true, the node will produce a LOT of output.
bool debug = this->declare_parameter("debug", false);
std::string debug_out_file = "robot_localization_debug.txt";
if (debug) {
try {
debug_out_file = this->declare_parameter("debug_out_file", debug_out_file);
debug_stream_.open(debug_out_file.c_str());
// Make sure we succeeded
if (debug_stream_.is_open()) {
filter_.setDebug(debug, &debug_stream_);
} else {
RCLCPP_ERROR_STREAM(
get_logger(),
"RosFilter<T>::loadParams() - unable to create debug output file " << debug_out_file);
}
} catch (const std::exception & e) {
RCLCPP_ERROR_STREAM(
get_logger(),
"RosFilter<T>::loadParams() - unable to create debug output file " << debug_out_file <<
". Error was " << e.what());
}
}
// These params specify the name of the robot's body frame (typically
// base_link) and odometry frame (typically odom)
map_frame_id_ = this->declare_parameter("map_frame", std::string("map"));
odom_frame_id_ = this->declare_parameter("odom_frame", std::string("odom"));
base_link_frame_id_ = this->declare_parameter(
"base_link_frame",
std::string("base_link"));
base_link_output_frame_id_ = this->declare_parameter(
"base_link_frame_output",
base_link_frame_id_);
/*
* These parameters are designed to enforce compliance with REP-105:
* http://www.ros.org/reps/rep-0105.html
* When fusing absolute position data from sensors such as GPS, the state
* estimate can undergo discrete jumps. According to REP-105, we want three
* coordinate frames: map, odom, and base_link. The map frame can have
* discontinuities, but is the frame with the most accurate position estimate
* for the robot and should not suffer from drift. The odom frame drifts over
* time, but is guaranteed to be continuous and is accurate enough for local
* planning and navigation. The base_link frame is affixed to the robot. The
* intention is that some odometry source broadcasts the odom->base_link
* transform. The localization software should broadcast map->base_link.
* However, tf does not allow multiple parents for a coordinate frame, so
* we must *compute* map->base_link, but then use the existing odom->base_link
* transform to compute *and broadcast* map->odom.
*
* The state estimation nodes in robot_localization therefore have two
* "modes." If your world_frame parameter value matches the odom_frame
* parameter value, then robot_localization will assume someone else is
* broadcasting a transform from odom_frame->base_link_frame, and it will
* compute the map_frame->odom_frame transform. Otherwise, it will simply
* compute the odom_frame->base_link_frame transform.
*
* The default is the latter behavior (broadcast of odom->base_link).
*/
world_frame_id_ = this->declare_parameter("world_frame", odom_frame_id_);
if (map_frame_id_ == odom_frame_id_ ||
odom_frame_id_ == base_link_frame_id_ ||
map_frame_id_ == base_link_frame_id_ ||
odom_frame_id_ == base_link_output_frame_id_ ||
map_frame_id_ == base_link_output_frame_id_)
{
RCLCPP_ERROR(
get_logger(),
"Invalid frame configuration! The values for map_frame, "
"odom_frame, and base_link_frame must be unique. If using a base_link_frame_output "
"values, it must not match the map_frame or odom_frame.");
}
// Try to resolve tf_prefix
std::string tf_prefix = "";
std::string tf_prefix_path = "";
this->declare_parameter("tf_prefix", rclcpp::PARAMETER_STRING);
if (this->get_parameter("tf_prefix", tf_prefix_path)) {
// Append the tf prefix in a tf2-friendly manner
filter_utilities::appendPrefix(tf_prefix, map_frame_id_);
filter_utilities::appendPrefix(tf_prefix, odom_frame_id_);
filter_utilities::appendPrefix(tf_prefix, base_link_frame_id_);
filter_utilities::appendPrefix(tf_prefix, base_link_output_frame_id_);
filter_utilities::appendPrefix(tf_prefix, world_frame_id_);
}
// Whether we're publshing the world_frame->base_link_frame transform
publish_transform_ = this->declare_parameter("publish_tf", true);
// Whether we're publishing the acceleration state transform
publish_acceleration_ = this->declare_parameter("publish_acceleration", false);
// Whether we'll allow old measurements to cause a re-publication of the updated state
permit_corrected_publication_ = this->declare_parameter("permit_corrected_publication", false);
// Transform future dating
double offset_tmp = this->declare_parameter("transform_time_offset", 0.0);
tf_time_offset_ = rclcpp::Duration::from_seconds(offset_tmp);
// Transform timeout
double timeout_tmp = this->declare_parameter("transform_timeout", 0.0);
tf_timeout_ = rclcpp::Duration::from_seconds(timeout_tmp);
// Update frequency and sensor timeout
frequency_ = this->declare_parameter("frequency", 30.0);
predict_to_current_time_ = this->declare_parameter<bool>("predict_to_current_time", false);
sensor_timeout_ =
rclcpp::Duration::from_seconds(this->declare_parameter("sensor_timeout", 1.0 / frequency_));
filter_.setSensorTimeout(sensor_timeout_);
// Determine if we're in 2D mode
two_d_mode_ = this->declare_parameter("two_d_mode", false);
// Smoothing window size
smooth_lagged_data_ = this->declare_parameter("smooth_lagged_data", false);
double history_length_double = this->declare_parameter("history_length", 0.0);
if (!smooth_lagged_data_ && std::abs(history_length_double) > 0) {
RCLCPP_ERROR_STREAM(
get_logger(),
"Filter history interval of " << history_length_double <<
" specified, but smooth_lagged_data is set to false. Lagged data will not be smoothed.");
}
if (smooth_lagged_data_ && history_length_double < 0) {
RCLCPP_ERROR_STREAM(
get_logger(),
"Negative history interval of " << history_length_double << " specified. Absolute value "
"will be assumed.");
}
history_length_ = rclcpp::Duration::from_seconds(std::abs(history_length_double));
// Whether we reset filter on jump back in time
reset_on_time_jump_ = this->declare_parameter("reset_on_time_jump", false);
// Determine if we're using a control term
double control_timeout = sensor_timeout_.seconds();
std::vector<bool> control_update_vector;
std::vector<double> acceleration_limits;
std::vector<double> acceleration_gains;
std::vector<double> deceleration_limits;
std::vector<double> deceleration_gains;
use_control_ = this->declare_parameter("use_control", false);
control_timeout = this->declare_parameter("control_timeout", 0.0);
if (use_control_) {
this->declare_parameter("control_config", rclcpp::PARAMETER_BOOL_ARRAY);
if (this->get_parameter("control_config", control_update_vector)) {
if (control_update_vector.size() != TWIST_SIZE) {
RCLCPP_ERROR_STREAM(
get_logger(),
"Control configuration must be of size " <<
TWIST_SIZE << ". Provided config was of size " << control_update_vector.size() <<
". No control term will be used.");
use_control_ = false;
}
} else {
RCLCPP_ERROR_STREAM(
get_logger(),
"use_control is set to true, but control_config is missing. No control term will be "
"used.");
use_control_ = false;
}
this->declare_parameter("acceleration_limits", rclcpp::PARAMETER_DOUBLE_ARRAY);
if (this->get_parameter("acceleration_limits", acceleration_limits)) {
if (acceleration_limits.size() != TWIST_SIZE) {
RCLCPP_ERROR_STREAM(
get_logger(),
"Acceleration configuration must be of size " << TWIST_SIZE <<