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Hello!!

I have to fuse two sources: IMU data (accelerometer and gyroscope) and another source that provides the difference between the previous position and the current one by giving a traslation vector and a quaternion.

IMU data is in baselink frame and the other source is in the reference frame (I selected odom). I thought of using a PoseWithCovarianceStamped for the second source, but I'm not sure if robot localization works with variations in pose, so the questions are:

Is there any way I can fuse them with robot_localization? As the second source does not give a complete pose but a change in pose do I have to modify this data? Should I add all the previous estimations plus the current one to get a the "global" pose (instead of the variation) and send that one to EKF along with the IMU?

Thank you.

EDIT:

I was trying to fuse just my estimation to see how it performed, this is the yaml code:

frequency: 5
sensor_timeout: 2

two_d_mode: false

transform_time_offset: 0.0
transform_timeout: 0.0

print_diagnostics: false
debug: true
debug_out_file: /home/javier/pruebas/MasPruebas/Data/file.txt
publish_tf: true
publish_acceleration: false

map_frame: map              # Defaults to "map" if unspecified
odom_frame: odom            # Defaults to "odom" if unspecified
base_link_frame: erlecopter/imu_link  # Defaults to "base_link" if unspecified
world_frame: odom           # Defaults to the value of odom_frame if unspecified

odom0: /Camera_estimation
odom0_config:  [false,  false,  false,
                false,  false,  false,
                true,  true,   true,
                true,  true,  true,
                false,  false,  false]
odom0_differential: false;
odom0_relative: true

odom0_queue_size: 2


#imu0: /erlecopter/imu
#imu0_config:  [false, false, false,
#               false, false, false,
#               false, false, false,
#               true,  true,  true,
#               true, true, true]
#imu0_relative: true
#imu0_queue_size: 100


process_noise_covariance:  [diag(small values)]


initial_estimate_covariance: [diag(1e-6)]

And the main code is something like this:

void ImageCallback()
{
   /*Estimate displacements*/

      double interval=(stamp-Prev_time).toSec();

      nav_msgs::Odometry pub_msg;
      pub_msg.header.seq=seq;
      pub_msg.header.stamp=stamp;
      pub_msg.header.frame_id="odom";

      pub_msg.child_frame_id="odom";

      pub_msg.twist.twist.linear.x=tras.at<double>(0)/interval;
      pub_msg.twist.twist.linear.y=tras.at<double>(1)/interval;
      pub_msg.twist.twist.linear.z=tras.at<double>(2)/interval;

      pub_msg.twist.twist.angular.x=(angles.roll-Prev_angles.roll)/interval;
      pub_msg.twist.twist.angular.y=(angles.pitch-Prev_angles.pitch)/interval;
      pub_msg.twist.twist.angular.z=(angles.yaw-Prev_angles.yaw)/interval;

      pub_msg.twist.covariance=Covariance_default;

      Pub_estimation.publish(pub_msg);

      Prev_time=stamp;

}

int main(int argc, char **argv)
{
  ros::init(argc, argv, "image2angles");
  ros::NodeHandle nh;
  image_transport::ImageTransport it(nh);
  image_transport::Subscriber sub_img = it.subscribe("/erlecopter/bottom/image_raw", 1, imageCallback);
  ros::Subscriber sub_h = nh.subscribe("/erlecopter/imu", 1, imuCallback);
  ros::Subscriber sub_h2 = nh.subscribe("/odometry/filtered", 1, ScaleCallback);
  Pub_estimation=nh.advertise<nav_msgs::Odometry>("Camera_estimation",1);
  ros::spin();
}

I've checked the Camera_estimation topic and the messages are being published, but odometry/filtered isn't publishing anything.

Hello!!!

I'm using robot_localization for 3D positioning and I'm giving it a try with one source. In this case, it is providing a TwistWithCovarianceStamped in odom frame and I'm trying to use the EKF just to see how it works, but it return a straight line. In the debug file it says

Could not transform measurement into erlecopter/imu_link. Ignoring...

• I renamed base_link as erlecopter/imu_link

  Does anyone have any idea regarding how to solve this problem? The TwistWithCovarianceStamped message is properly sent.

I'll add the params file:

frequency: 5
sensor_timeout: 2

two_d_mode: false


transform_time_offset: 0.0
transform_timeout: 0.0

print_diagnostics: false
debug: true
debug_out_file: /home/javier/pruebas/MasPruebas/Data/file.txt
publish_tf: true
publish_acceleration: false

map_frame: map              # Defaults to "map" if unspecified
odom_frame: odom            # Defaults to "odom" if unspecified
base_link_frame: erlecopter/imu_link  # Defaults to "base_link" if unspecified
world_frame: odom           # Defaults to the value of odom_frame if unspecified


twist0: /Camera_estimation
twist0_config:  [false, false, false,
                false,  false,  false,
                true, true, true,
                true, true, true,
                false,  false,  false]
twist0_queue_size: 3

process_noise_covariance: ...
initial_estimate_covariance: ...

I finally opted for not simulating the camera alone and mixed it with the IMU, then it worked (better and IMU-only). Don

Try to change it, and see what happens. -> It returns a completely wrong output. I have not touched the urdf file nor

No, I used odom because the information given was in odom frame of reference.

It just says no tf data received. Shouldn't robot_localization provide this odom to base_link transform from the given d

Robot_localization - Could not transform measurement into base_link Hello!!! I'm using robot_localization for 3D positi

I'll take a look at that link. The odometry data is nowhere near those extreme outputs values that I get through EKF. (I

I'm trying to use robot_localization package to fuse IMU and a visual estimation but, when I try to run the .launch file, it returns an error.

Terminal output:

SUMMARY
========

CLEAR PARAMETERS
 * /ekf_se/

PARAMETERS
 * /ekf_se/base_link_frame: erlecopter/base_link
 * /ekf_se/control_config: [False, False, Fa...
 * /ekf_se/control_timeout: 0.2
 * /ekf_se/debug: False
 * /ekf_se/debug_out_file: /home/javier/prue...
 * /ekf_se/frequency: 30
 * /ekf_se/imu0: /erlecopter/imu
 * /ekf_se/imu0_config: [False, False, Fa...
 * /ekf_se/imu0_differential: False
 * /ekf_se/imu0_nodelay: False
 * /ekf_se/imu0_queue_size: 5
 * /ekf_se/imu0_relative: False
 * /ekf_se/imu0_remove_gravitational_acceleration: True
 * /ekf_se/initial_estimate_covariance: ['1e-9', 0, 0, 0,...
 * /ekf_se/map_frame: map
 * /ekf_se/odom_frame: odom
 * /ekf_se/pose0: Camera_estimation
 * /ekf_se/pose0_config: [True, True, True...
 * /ekf_se/pose0_differential: True
 * /ekf_se/pose0_nodelay: False
 * /ekf_se/pose0_queue_size: 1
 * /ekf_se/pose0_relative: False
 * /ekf_se/print_diagnostics: True
 * /ekf_se/process_noise_covariance: [0.05, 0, 0, 0, 0...
 * /ekf_se/publish_acceleration: False
 * /ekf_se/publish_tf: True
 * /ekf_se/sensor_timeout: 0.6
 * /ekf_se/stamped_control: False
 * /ekf_se/transform_time_offset: 0.0
 * /ekf_se/transform_timeout: 0.0
 * /ekf_se/two_d_mode: False
 * /ekf_se/use_control: False
 * /ekf_se/world_frame: odom
 * /rosdistro: indigo
 * /rosversion: 1.11.21

NODES
  /
    ekf_se (robot_localization/ekf_localization_node)

ROS_MASTER_URI=http://localhost:11311

core service [/rosout] found
ERROR: cannot launch node of type [robot_localization/ekf_localization_node]: robot_localization
ROS path [0]=/opt/ros/indigo/share/ros
ROS path [1]=/home/javier/pruebas/MasPruebas/useful_info/catkin_builts/src
ROS path [2]=/home/javier/simulation/ros_catkin_WS/src/catkin_simple
// More paths
ROS path [40]=/opt/ros/indigo/share
ROS path [41]=/opt/ros/indigo/stacks
No processes to monitor
shutting down processing monitor...

This is the launch file, and it refers to a yaml (ekf_template.yaml) file with all the parameters:

<launch>
  <node pkg="robot_localization" type="ekf_localization_node" name="ekf_se" clear_params="true">
    <rosparam command="load" file="$(find useful_info)/ekf_template.yaml" />

   <!--  Placeholder for output topic remapping
    <remap from="odometry/filtered" to=""/>
    <remap from="accel/filtered" to=""/>
    -->

  </node>
</launch>

Yaml file:

frequency: 30

sensor_timeout: 0.6

two_d_mode: false

transform_time_offset: 0.0
transform_timeout: 0.0

print_diagnostics: true

debug: false
debug_out_file: /home/javier/pruebas/MasPruebas/Data/Fallos.txt

publish_tf: true
publish_acceleration: false


map_frame: map              # Defaults to "map" if unspecified
odom_frame: odom            # Defaults to "odom" if unspecified
base_link_frame: erlecopter/base_link  # Defaults to "base_link" if unspecified
world_frame: odom           # Defaults to the value of odom_frame if unspecified


pose0: Camera_estimation
pose0_config: [true,  true,  true,
               true,  true,  true,
               false, false, false,
               false, false, false,
               false, false, false]
pose0_differential: true
pose0_relative: false
pose0_queue_size: 1
pose0_nodelay: false

imu0: /erlecopter/imu
imu0_config: [false, false, false,
               false, false, false,
               false, false, false,
               true,  true,  true,
               true,  true,  true]
imu0_differential: false
imu0_relative: false
imu0_queue_size: 5
imu0_nodelay: false
imu0_remove_gravitational_acceleration: true

use_control: false
stamped_control: false

process_noise_covariance: [numbers_matrix]

initial_estimate_covariance: [numbers_matrix]

Ubuntu 14.04 ROS Indigo

If I use yours (taking into account that mine is not a 2D movement) it does work, but it returns an output whose displac

I've added the relevant parts of the code to the main question

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Error when launching ekf_localization_node I'm trying to use robot_localization package to fuse IMU and a visual estimat

Hello! I've got a couple of questions:

1. I've been reading robot_localization documentation (http://docs.ros.org/melodic/api/robot...) and I've seen that they only use North-East-Up references:

As of this writing, robot_localization assumes an ENU frame for all IMU data, and does not work with NED frame data. This may change in the future, but for now, users should ensure that data is transformed to the ENU frame before using it with any node in robot_localization.

Nontheless, in that same page, if you look at IMU information, they say:

The state estimation nodes in robot_localization assume that an IMU that is placed in its neutral right-side-up position on a flat surface will: Measure +9.81 meters per second squared for the Z axis.

In that case, if gravity is possitive, the Z vector is pointing down. Isn't this the opposite of what was stated above? Should my IMU gravity component be positive(NED) or negative(ENU)?

.2 I am measuring the variation of the angles and the movement from images taken from the bottom camera of a UAV and fusing them with the IMU. IMU updates values each 2.5ms and the information from the image is extracted each ~100ms, so the pose information from the camera is given with respect to the previous frame (~100ms ago). Does the robot_localization takes into account that pose values are with respect to the previous meassure given by that sensor, or should I change something?

Thank you in advance.