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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.h"
#include "robot_localization/filter_utilities.h"
#include "robot_localization/ekf.h"
#include "robot_localization/ukf.h"
#include <tf2_geometry_msgs/tf2_geometry_msgs.h>
#include <algorithm>
#include <iostream>
#include <map>
#include <memory>
#include <string>
#include <utility>
#include <vector>
#include <limits>
#if defined(_WIN32) && defined(ERROR)
#undef ERROR
#endif
namespace RobotLocalization
{
template<typename T>
RosFilter<T>::RosFilter(ros::NodeHandle nh,
ros::NodeHandle nh_priv,
std::string node_name,
std::vector<double> args) :
disabledAtStartup_(false),
enabled_(false),
predictToCurrentTime_(false),
printDiagnostics_(true),
publishAcceleration_(false),
publishTransform_(true),
resetOnTimeJump_(false),
smoothLaggedData_(false),
toggledOn_(true),
twoDMode_(false),
useControl_(false),
dynamicDiagErrorLevel_(diagnostic_msgs::DiagnosticStatus::OK),
staticDiagErrorLevel_(diagnostic_msgs::DiagnosticStatus::OK),
frequency_(30.0),
gravitationalAcc_(9.80665),
historyLength_(0),
minFrequency_(frequency_ - 2.0),
maxFrequency_(frequency_ + 2.0),
baseLinkFrameId_("base_link"),
mapFrameId_("map"),
odomFrameId_("odom"),
worldFrameId_(odomFrameId_),
lastDiagTime_(0),
lastSetPoseTime_(0),
latestControlTime_(0),
tfTimeOffset_(ros::Duration(0)),
tfTimeout_(ros::Duration(0)),
filter_(args),
nh_(nh),
nhLocal_(nh_priv),
diagnosticUpdater_(nh, nh_priv, node_name),
tfListener_(tfBuffer_)
{
stateVariableNames_.push_back("X");
stateVariableNames_.push_back("Y");
stateVariableNames_.push_back("Z");
stateVariableNames_.push_back("ROLL");
stateVariableNames_.push_back("PITCH");
stateVariableNames_.push_back("YAW");
stateVariableNames_.push_back("X_VELOCITY");
stateVariableNames_.push_back("Y_VELOCITY");
stateVariableNames_.push_back("Z_VELOCITY");
stateVariableNames_.push_back("ROLL_VELOCITY");
stateVariableNames_.push_back("PITCH_VELOCITY");
stateVariableNames_.push_back("YAW_VELOCITY");
stateVariableNames_.push_back("X_ACCELERATION");
stateVariableNames_.push_back("Y_ACCELERATION");
stateVariableNames_.push_back("Z_ACCELERATION");
diagnosticUpdater_.setHardwareID("none");
}
template<typename T>
RosFilter<T>::RosFilter(ros::NodeHandle nh, ros::NodeHandle nh_priv, std::vector<double> args) :
RosFilter<T>::RosFilter(nh, nh_priv, ros::this_node::getName(), args)
{}
template<typename T>
RosFilter<T>::~RosFilter()
{
topicSubs_.clear();
}
template<typename T>
void RosFilter<T>::initialize()
{
loadParams();
if (printDiagnostics_)
{
diagnosticUpdater_.add("Filter diagnostic updater", this, &RosFilter<T>::aggregateDiagnostics);
}
// Set up the frequency diagnostic
minFrequency_ = frequency_ - 2;
maxFrequency_ = frequency_ + 2;
freqDiag_ = std::make_unique<diagnostic_updater::HeaderlessTopicDiagnostic>(
"odometry/filtered",
diagnosticUpdater_,
diagnostic_updater::FrequencyStatusParam(
&minFrequency_,
&maxFrequency_,
0.1,
10));
// Publisher
positionPub_ = nh_.advertise<nav_msgs::Odometry>("odometry/filtered", 20);
// Optional acceleration publisher
if (publishAcceleration_)
{
accelPub_ = nh_.advertise<geometry_msgs::AccelWithCovarianceStamped>("accel/filtered", 20);
}
lastDiagTime_ = ros::Time::now();
periodicUpdateTimer_ = nh_.createTimer(ros::Duration(1./frequency_), &RosFilter<T>::periodicUpdate, this);
}
template<typename T>
void RosFilter<T>::reset()
{
// Get rid of any initial poses (pretend we've never had a measurement)
initialMeasurements_.clear();
previousMeasurements_.clear();
previousMeasurementCovariances_.clear();
clearMeasurementQueue();
filterStateHistory_.clear();
measurementHistory_.clear();
// Also set the last set pose time, so we ignore all messages
// that occur before it
lastSetPoseTime_ = ros::Time(0);
// clear tf buffer to avoid TF_OLD_DATA errors
tfBuffer_.clear();
// clear last message timestamp, so older messages will be accepted
lastMessageTimes_.clear();
// reset filter to uninitialized state
filter_.reset();
// clear all waiting callbacks
ros::getGlobalCallbackQueue()->clear();
}
template<typename T>
bool RosFilter<T>::toggleFilterProcessingCallback(robot_localization::ToggleFilterProcessing::Request& req,
robot_localization::ToggleFilterProcessing::Response& resp)
{
if (req.on == toggledOn_)
{
ROS_WARN_STREAM("Service was called to toggle filter processing but state was already as requested.");
resp.status = false;
}
else
{
ROS_INFO("Toggling filter measurement filtering to %s.", req.on ? "On" : "Off");
toggledOn_ = req.on;
resp.status = true;
}
return true;
}
// @todo: Replace with AccelWithCovarianceStamped
template<typename T>
void RosFilter<T>::accelerationCallback(const sensor_msgs::Imu::ConstPtr &msg, const CallbackData &callbackData,
const std::string &targetFrame)
{
// If we've just reset the filter, then we want to ignore any messages
// that arrive with an older timestamp
if (msg->header.stamp <= lastSetPoseTime_)
{
return;
}
const std::string &topicName = callbackData.topicName_;
RF_DEBUG("------ RosFilter::accelerationCallback (" << topicName << ") ------\n"
"Twist message:\n" << *msg);
if (lastMessageTimes_.count(topicName) == 0)
{
lastMessageTimes_.insert(std::pair<std::string, ros::Time>(topicName, msg->header.stamp));
}
// Make sure this message is newer than the last one
if (msg->header.stamp >= lastMessageTimes_[topicName])
{
RF_DEBUG("Update vector for " << topicName << " is:\n" << topicName);
Eigen::VectorXd measurement(STATE_SIZE);
Eigen::MatrixXd measurementCovariance(STATE_SIZE, STATE_SIZE);
measurement.setZero();
measurementCovariance.setZero();
// Make sure we're actually updating at least one of these variables
std::vector<int> updateVectorCorrected = callbackData.updateVector_;
// Prepare the twist data for inclusion in the filter
if (prepareAcceleration(msg, topicName, targetFrame, updateVectorCorrected, measurement,
measurementCovariance))
{
// 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(topicName,
measurement,
measurementCovariance,
updateVectorCorrected,
callbackData.rejectionThreshold_,
msg->header.stamp);
RF_DEBUG("Enqueued new measurement for " << topicName << "_acceleration\n");
}
else
{
RF_DEBUG("Did *not* enqueue measurement for " << topicName << "_acceleration\n");
}
lastMessageTimes_[topicName] = msg->header.stamp;
RF_DEBUG("Last message time for " << topicName << " is now " <<
lastMessageTimes_[topicName] << "\n");
}
else if (resetOnTimeJump_ && ros::Time::isSimTime())
{
reset();
}
else
{
std::stringstream stream;
stream << "The " << topicName << " 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.toSec() << ")";
addDiagnostic(diagnostic_msgs::DiagnosticStatus::WARN,
topicName + "_timestamp",
stream.str(),
false);
RF_DEBUG("Message is too old. Last message time for " << topicName <<
" is " << lastMessageTimes_[topicName] << ", current message time is " <<
msg->header.stamp << ".\n");
}
RF_DEBUG("\n----- /RosFilter::accelerationCallback (" << topicName << ") ------\n");
}
template<typename T>
void RosFilter<T>::controlCallback(const geometry_msgs::Twist::ConstPtr &msg)
{
geometry_msgs::TwistStampedPtr twistStampedPtr = geometry_msgs::TwistStampedPtr(new geometry_msgs::TwistStamped());
twistStampedPtr->twist = *msg;
twistStampedPtr->header.frame_id = baseLinkFrameId_;
twistStampedPtr->header.stamp = ros::Time::now();
controlCallback(twistStampedPtr);
}
template<typename T>
void RosFilter<T>::controlCallback(const geometry_msgs::TwistStamped::ConstPtr &msg)
{
if (msg->header.frame_id == baseLinkFrameId_ || msg->header.frame_id == "")
{
latestControl_(ControlMemberVx) = msg->twist.linear.x;
latestControl_(ControlMemberVy) = msg->twist.linear.y;
latestControl_(ControlMemberVz) = msg->twist.linear.z;
latestControl_(ControlMemberVroll) = msg->twist.angular.x;
latestControl_(ControlMemberVpitch) = msg->twist.angular.y;
latestControl_(ControlMemberVyaw) = msg->twist.angular.z;
latestControlTime_ = msg->header.stamp;
// Update the filter with this control term
filter_.setControl(latestControl_, msg->header.stamp.toSec());
}
else
{
ROS_WARN_STREAM_THROTTLE(5.0, "Commanded velocities must be given in the robot's body frame (" <<
baseLinkFrameId_ << "). Message frame was " << msg->header.frame_id);
}
}
template<typename T>
void RosFilter<T>::enqueueMeasurement(const std::string &topicName,
const Eigen::VectorXd &measurement,
const Eigen::MatrixXd &measurementCovariance,
const std::vector<int> &updateVector,
const double mahalanobisThresh,
const ros::Time &time)
{
MeasurementPtr meas = MeasurementPtr(new Measurement());
meas->topicName_ = topicName;
meas->measurement_ = measurement;
meas->covariance_ = measurementCovariance;
meas->updateVector_ = updateVector;
meas->time_ = time.toSec();
meas->mahalanobisThresh_ = mahalanobisThresh;
meas->latestControl_ = latestControl_;
meas->latestControlTime_ = latestControlTime_.toSec();
measurementQueue_.push(meas);
}
template<typename T>
void RosFilter<T>::forceTwoD(Eigen::VectorXd &measurement,
Eigen::MatrixXd &measurementCovariance,
std::vector<int> &updateVector)
{
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;
measurementCovariance(StateMemberZ, StateMemberZ) = 1e-6;
measurementCovariance(StateMemberRoll, StateMemberRoll) = 1e-6;
measurementCovariance(StateMemberPitch, StateMemberPitch) = 1e-6;
measurementCovariance(StateMemberVz, StateMemberVz) = 1e-6;
measurementCovariance(StateMemberVroll, StateMemberVroll) = 1e-6;
measurementCovariance(StateMemberVpitch, StateMemberVpitch) = 1e-6;
measurementCovariance(StateMemberAz, StateMemberAz) = 1e-6;
updateVector[StateMemberZ] = 1;
updateVector[StateMemberRoll] = 1;
updateVector[StateMemberPitch] = 1;
updateVector[StateMemberVz] = 1;
updateVector[StateMemberVroll] = 1;
updateVector[StateMemberVpitch] = 1;
updateVector[StateMemberAz] = 1;
}
template<typename T>
bool RosFilter<T>::getFilteredOdometryMessage(nav_msgs::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 &estimateErrorCovariance = 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] = estimateErrorCovariance(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] =
estimateErrorCovariance(i + POSITION_V_OFFSET, j + POSITION_V_OFFSET);
}
}
message.header.stamp = ros::Time(filter_.getLastMeasurementTime());
message.header.frame_id = worldFrameId_;
message.child_frame_id = baseLinkOutputFrameId_;
}
return filter_.getInitializedStatus();
}
template<typename T>
bool RosFilter<T>::getFilteredAccelMessage(geometry_msgs::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 &estimateErrorCovariance = 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);
// 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] =
estimateErrorCovariance(i + POSITION_A_OFFSET, j + POSITION_A_OFFSET);
}
}
// Fill header information
message.header.stamp = ros::Time(filter_.getLastMeasurementTime());
message.header.frame_id = baseLinkOutputFrameId_;
}
return filter_.getInitializedStatus();
}
template<typename T>
void RosFilter<T>::imuCallback(const sensor_msgs::Imu::ConstPtr &msg,
const std::string &topicName,
const CallbackData &poseCallbackData,
const CallbackData &twistCallbackData,
const CallbackData &accelCallbackData)
{
RF_DEBUG("------ RosFilter::imuCallback (" << topicName << ") ------\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 (msg->header.stamp <= lastSetPoseTime_)
{
std::stringstream stream;
stream << "The " << topicName << " 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.toSec() << ")";
addDiagnostic(diagnostic_msgs::DiagnosticStatus::WARN,
topicName + "_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 (poseCallbackData.updateSum_ > 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 (::fabs(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::PoseWithCovarianceStamped *posPtr = new geometry_msgs::PoseWithCovarianceStamped();
posPtr->header = msg->header;
posPtr->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++)
{
posPtr->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 baseLinkFrameId_ and tell the poseCallback that it is
// working with IMU data. This will cause it to apply different logic to the data.
geometry_msgs::PoseWithCovarianceStampedConstPtr pptr(posPtr);
poseCallback(pptr, poseCallbackData, baseLinkFrameId_,
baseLinkFrameId_, true);
}
}
if (twistCallbackData.updateSum_ > 0)
{
// Ignore rotational velocity if the first covariance value is -1
if (::fabs(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::TwistWithCovarianceStamped *twistPtr = new geometry_msgs::TwistWithCovarianceStamped();
twistPtr->header = msg->header;
twistPtr->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++)
{
twistPtr->twist.covariance[TWIST_SIZE * (i + ORIENTATION_SIZE) + (j + ORIENTATION_SIZE)] =
msg->angular_velocity_covariance[ORIENTATION_SIZE * i + j];
}
}
geometry_msgs::TwistWithCovarianceStampedConstPtr tptr(twistPtr);
twistCallback(tptr, twistCallbackData, baseLinkFrameId_);
}
}
if (accelCallbackData.updateSum_ > 0)
{
// Ignore linear acceleration if the first covariance value is -1
if (::fabs(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, accelCallbackData, baseLinkFrameId_);
}
}
RF_DEBUG("\n----- /RosFilter::imuCallback (" << topicName << ") ------\n");
}
template<typename T>
void RosFilter<T>::integrateMeasurements(const ros::Time ¤tTime)
{
const double currentTimeSec = currentTime.toSec();
RF_DEBUG("------ RosFilter::integrateMeasurements ------\n\n"
"Integration time is " << std::setprecision(20) << currentTimeSec << "\n"
<< measurementQueue_.size() << " measurements in queue.\n");
bool predictToCurrentTime = predictToCurrentTime_;
// If we have any measurements in the queue, process them
if (!measurementQueue_.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& firstMeasurement = measurementQueue_.top();
int restoredMeasurementCount = 0;
if (smoothLaggedData_ && firstMeasurement->time_ < filter_.getLastMeasurementTime())
{
RF_DEBUG("Received a measurement that was " << filter_.getLastMeasurementTime() - firstMeasurement->time_ <<
" seconds in the past. Reverting filter state and measurement queue...");
int originalCount = static_cast<int>(measurementQueue_.size());
const double firstMeasurementTime = firstMeasurement->time_;
const std::string firstMeasurementTopic = firstMeasurement->topicName_;
if (!revertTo(firstMeasurementTime - 1e-9)) // revertTo may invalidate firstMeasurement
{
RF_DEBUG("ERROR: history interval is too small to revert to time " << firstMeasurementTime << "\n");
ROS_WARN_STREAM_DELAYED_THROTTLE(historyLength_, "Received old measurement for topic " <<
firstMeasurementTopic << ", but history interval is insufficiently sized. Measurement time is " <<
std::setprecision(20) << firstMeasurementTime << ", current time is " << currentTime.toSec() <<
", history length is " << historyLength_ << ".");
restoredMeasurementCount = 0;
}
restoredMeasurementCount = static_cast<int>(measurementQueue_.size()) - originalCount;
}
while (!measurementQueue_.empty() && ros::ok())
{
MeasurementPtr measurement = measurementQueue_.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 (measurement->time_ > currentTime.toSec())
{
break;
}
measurementQueue_.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 (useControl_ && restoredMeasurementCount > 0)
{
filter_.setControl(measurement->latestControl_, measurement->latestControlTime_);
restoredMeasurementCount--;
}
// This will call predict and, if necessary, correct
filter_.processMeasurement(*(measurement.get()));
// Store old states and measurements if we're smoothing
if (smoothLaggedData_)
{
// Invariant still holds: measurementHistoryDeque_.back().time_ < measurementQueue_.top().time_
measurementHistory_.push_back(measurement);
// We should only save the filter state once per unique timstamp
if (measurementQueue_.empty() ||
::fabs(measurementQueue_.top()->time_ - filter_.getLastMeasurementTime()) > 1e-9)
{
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.
double lastUpdateDelta = currentTimeSec - filter_.getLastMeasurementTime();
// If we get a large delta, then continuously predict until
if (lastUpdateDelta >= filter_.getSensorTimeout())
{
predictToCurrentTime = true;
RF_DEBUG("Sensor timeout! Last measurement time was " << filter_.getLastMeasurementTime() <<
", current time is " << currentTimeSec <<
", delta is " << lastUpdateDelta << "\n");
}
}
else
{
RF_DEBUG("Filter not yet initialized.\n");
}
if (filter_.getInitializedStatus() && predictToCurrentTime)
{
double lastUpdateDelta = currentTimeSec - filter_.getLastMeasurementTime();
filter_.validateDelta(lastUpdateDelta);
filter_.predict(currentTimeSec, lastUpdateDelta);
// Update the last measurement time and last update time
filter_.setLastMeasurementTime(filter_.getLastMeasurementTime() + lastUpdateDelta);
}
RF_DEBUG("\n----- /RosFilter::integrateMeasurements ------\n");
}
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> absPoseVarCounts;
absPoseVarCounts[StateMemberX] = 0;
absPoseVarCounts[StateMemberY] = 0;
absPoseVarCounts[StateMemberZ] = 0;
absPoseVarCounts[StateMemberRoll] = 0;
absPoseVarCounts[StateMemberPitch] = 0;
absPoseVarCounts[StateMemberYaw] = 0;
// Same for twist variables
std::map<StateMembers, int> twistVarCounts;
twistVarCounts[StateMemberVx] = 0;
twistVarCounts[StateMemberVy] = 0;
twistVarCounts[StateMemberVz] = 0;
twistVarCounts[StateMemberVroll] = 0;
twistVarCounts[StateMemberVpitch] = 0;
twistVarCounts[StateMemberVyaw] = 0;
// Determine if we'll be printing diagnostic information
nhLocal_.param("print_diagnostics", printDiagnostics_, true);
// Check for custom gravitational acceleration value
nhLocal_.param("gravitational_acceleration", gravitationalAcc_, 9.80665);
// Grab the debug param. If true, the node will produce a LOT of output.
bool debug;
nhLocal_.param("debug", debug, false);
if (debug)
{
std::string debugOutFile;
try
{
nhLocal_.param("debug_out_file", debugOutFile, std::string("robot_localization_debug.txt"));
debugStream_.open(debugOutFile.c_str());
// Make sure we succeeded
if (debugStream_.is_open())
{
filter_.setDebug(debug, &debugStream_);
}
else
{
ROS_WARN_STREAM("RosFilter::loadParams() - unable to create debug output file " << debugOutFile);
}
}
catch(const std::exception &e)
{
ROS_WARN_STREAM("RosFilter::loadParams() - unable to create debug output file" << debugOutFile
<< ". Error was " << e.what() << "\n");
}
}
// These params specify the name of the robot's body frame (typically
// base_link) and odometry frame (typically odom)
nhLocal_.param("map_frame", mapFrameId_, std::string("map"));
nhLocal_.param("odom_frame", odomFrameId_, std::string("odom"));
nhLocal_.param("base_link_frame", baseLinkFrameId_, std::string("base_link"));
nhLocal_.param("base_link_frame_output", baseLinkOutputFrameId_, baseLinkFrameId_);
/*
* 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).
*/
nhLocal_.param("world_frame", worldFrameId_, odomFrameId_);
ROS_FATAL_COND(mapFrameId_ == odomFrameId_ ||
odomFrameId_ == baseLinkFrameId_ ||
mapFrameId_ == baseLinkFrameId_ ||
odomFrameId_ == baseLinkOutputFrameId_ ||
mapFrameId_ == baseLinkOutputFrameId_,
"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 tfPrefix = "";
std::string tfPrefixPath = "";
if (nhLocal_.searchParam("tf_prefix", tfPrefixPath))
{
nhLocal_.getParam(tfPrefixPath, tfPrefix);
}
// Append the tf prefix in a tf2-friendly manner
FilterUtilities::appendPrefix(tfPrefix, mapFrameId_);
FilterUtilities::appendPrefix(tfPrefix, odomFrameId_);
FilterUtilities::appendPrefix(tfPrefix, baseLinkFrameId_);
FilterUtilities::appendPrefix(tfPrefix, baseLinkOutputFrameId_);
FilterUtilities::appendPrefix(tfPrefix, worldFrameId_);
// Whether we're publshing the world_frame->base_link_frame transform
nhLocal_.param("publish_tf", publishTransform_, true);
// Whether we're publishing the acceleration state transform
nhLocal_.param("publish_acceleration", publishAcceleration_, false);
// Whether we'll allow old measurements to cause a re-publication of the updated state
nhLocal_.param("permit_corrected_publication", permitCorrectedPublication_, false);
// Transform future dating
double offsetTmp;
nhLocal_.param("transform_time_offset", offsetTmp, 0.0);
tfTimeOffset_.fromSec(offsetTmp);
// Transform timeout
double timeoutTmp;
nhLocal_.param("transform_timeout", timeoutTmp, 0.0);
tfTimeout_.fromSec(timeoutTmp);
// Update frequency and sensor timeout
double sensorTimeout;
nhLocal_.param("frequency", frequency_, 30.0);
nhLocal_.param("sensor_timeout", sensorTimeout, 1.0 / frequency_);
filter_.setSensorTimeout(sensorTimeout);
// Determine if we're in 2D mode
nhLocal_.param("two_d_mode", twoDMode_, false);
// Smoothing window size
nhLocal_.param("smooth_lagged_data", smoothLaggedData_, false);
nhLocal_.param("history_length", historyLength_, 0.0);
// Wether we reset filter on jump back in time
nhLocal_.param("reset_on_time_jump", resetOnTimeJump_, false);
if (!smoothLaggedData_ && ::fabs(historyLength_) > 1e-9)
{
ROS_WARN_STREAM("Filter history interval of " << historyLength_ <<
" specified, but smooth_lagged_data is set to false. Lagged data will not be smoothed.");
}
if (smoothLaggedData_ && historyLength_ < -1e9)
{
ROS_WARN_STREAM("Negative history interval of " << historyLength_ <<
" specified. Absolute value will be assumed.");
}
historyLength_ = ::fabs(historyLength_);
nhLocal_.param("predict_to_current_time", predictToCurrentTime_, false);
// Determine if we're using a control term
bool stampedControl = false;
double controlTimeout = sensorTimeout;
std::vector<int> controlUpdateVector(TWIST_SIZE, 0);
std::vector<double> accelerationLimits(TWIST_SIZE, 1.0);
std::vector<double> accelerationGains(TWIST_SIZE, 1.0);
std::vector<double> decelerationLimits(TWIST_SIZE, 1.0);
std::vector<double> decelerationGains(TWIST_SIZE, 1.0);
nhLocal_.param("use_control", useControl_, false);
nhLocal_.param("stamped_control", stampedControl, false);
nhLocal_.param("control_timeout", controlTimeout, sensorTimeout);
if (useControl_)
{
if (nhLocal_.getParam("control_config", controlUpdateVector))
{
if (controlUpdateVector.size() != TWIST_SIZE)
{
ROS_ERROR_STREAM("Control configuration must be of size " << TWIST_SIZE << ". Provided config was of "
"size " << controlUpdateVector.size() << ". No control term will be used.");
useControl_ = false;
}
}
else
{
ROS_ERROR_STREAM("use_control is set to true, but control_config is missing. No control term will be used.");
useControl_ = false;
}
if (nhLocal_.getParam("acceleration_limits", accelerationLimits))
{
if (accelerationLimits.size() != TWIST_SIZE)
{
ROS_ERROR_STREAM("Acceleration configuration must be of size " << TWIST_SIZE << ". Provided config was of "
"size " << accelerationLimits.size() << ". No control term will be used.");
useControl_ = false;
}
}
else
{
ROS_WARN_STREAM("use_control is set to true, but acceleration_limits is missing. Will use default values.");
}
if (nhLocal_.getParam("acceleration_gains", accelerationGains))
{
const int size = accelerationGains.size();
if (size != TWIST_SIZE)
{
ROS_ERROR_STREAM("Acceleration gain configuration must be of size " << TWIST_SIZE <<
". Provided config was of size " << size << ". All gains will be assumed to be 1.");
std::fill_n(accelerationGains.begin(), std::min(size, TWIST_SIZE), 1.0);
accelerationGains.resize(TWIST_SIZE, 1.0);
}
}
if (nhLocal_.getParam("deceleration_limits", decelerationLimits))
{
if (decelerationLimits.size() != TWIST_SIZE)
{
ROS_ERROR_STREAM("Deceleration configuration must be of size " << TWIST_SIZE <<
". Provided config was of size " << decelerationLimits.size() << ". No control term will be used.");
useControl_ = false;
}
}
else
{
ROS_INFO_STREAM("use_control is set to true, but no deceleration_limits specified. Will use acceleration "
"limits.");
decelerationLimits = accelerationLimits;
}
if (nhLocal_.getParam("deceleration_gains", decelerationGains))
{
const int size = decelerationGains.size();
if (size != TWIST_SIZE)
{
ROS_ERROR_STREAM("Deceleration gain configuration must be of size " << TWIST_SIZE <<
". Provided config was of size " << size << ". All gains will be assumed to be 1.");
std::fill_n(decelerationGains.begin(), std::min(size, TWIST_SIZE), 1.0);
decelerationGains.resize(TWIST_SIZE, 1.0);
}
}
else
{
ROS_INFO_STREAM("use_control is set to true, but no deceleration_gains specified. Will use acceleration "
"gains.");
decelerationGains = accelerationGains;
}
}
bool dynamicProcessNoiseCovariance = false;
nhLocal_.param("dynamic_process_noise_covariance", dynamicProcessNoiseCovariance, false);
filter_.setUseDynamicProcessNoiseCovariance(dynamicProcessNoiseCovariance);
std::vector<double> initialState(STATE_SIZE, 0.0);
if (nhLocal_.getParam("initial_state", initialState))
{
if (initialState.size() != STATE_SIZE)
{
ROS_ERROR_STREAM("Initial state must be of size " << STATE_SIZE << ". Provided config was of size " <<
initialState.size() << ". The initial state will be ignored.");
}
else
{
Eigen::Map<Eigen::VectorXd> eigenState(initialState.data(), initialState.size());
filter_.setState(eigenState);
}
}
// Check if the filter should start or not
nhLocal_.param("disabled_at_startup", disabledAtStartup_, false);
enabled_ = !disabledAtStartup_;
// Check if tf warnings should be suppressed
nh_.getParam("/silent_tf_failure", tfSilentFailure_);
// Debugging writes to file
RF_DEBUG("tf_prefix is " << tfPrefix <<
"\nmap_frame is " << mapFrameId_ <<
"\nodom_frame is " << odomFrameId_ <<
"\nbase_link_frame is " << baseLinkFrameId_ <<
"\base_link_frame_output is " << baseLinkOutputFrameId_ <<
"\nworld_frame is " << worldFrameId_ <<
"\ntransform_time_offset is " << tfTimeOffset_.toSec() <<
"\ntransform_timeout is " << tfTimeout_.toSec() <<
"\nfrequency is " << frequency_ <<
"\nsensor_timeout is " << filter_.getSensorTimeout() <<