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I'm not be able to understand why I get a wrong pose from robot_pose_ekf. I'm using a kinect on a table and connected to the workstation to find the turtlebot position orientation and publish the result in /vo. Kinect is tracking a pattern marker and find a pose. I want to use odometry and imu to have a better result and have a pose when kinect can't see the marker.

My issue is /vo, /odom, and imu/data has similar orientation but ekf find something very different and I can't understand why.

Here is my tf tree to see how I work:

/vo:

header:    seq: 479   stamp: 
    secs: 1370526479
    nsecs: 1198623   frame_id: odom child_frame_id: base_footprint pose:   pose: 
    position: 
      x: 0.109848357611
      y: 0.0184851177529
      z: 0.00108856573807
    orientation: 
      x: -0.0155068317083
      y: 0.00236792210117
      z: -0.726064768505
      w: 0.687447367477   covariance: [0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 10000.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 10000.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-06]

/odom

header: 
  seq: 5144
  stamp: 
    secs: 1370526478
    nsecs: 985132932
  frame_id: odom
child_frame_id: base_footprint
pose: 
  pose: 
    position: 
      x: 0.122778090562
      y: 0.0180749352772
      z: 0.0
    orientation: 
      x: 0.0
      y: 0.0
      z: -0.754709580223
      w: 0.656059028991
  covariance: [0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1000000.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1000000.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1000000.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1000.0]

/imu/data:

header: 
  seq: 5154
  stamp: 
    secs: 1370526479
    nsecs: 318391084
  frame_id: gyro_link
orientation: 
  x: 0.0
  y: 0.0
  z: -0.641532116852
  w: 0.767096175878
orientation_covariance: [1000000.0, 0.0, 0.0, 0.0, 1000000.0, 0.0, 0.0, 0.0, 1e-06]

and robot_pose_ekf/odom:

header: 
  seq: 3340
  stamp: 
    secs: 1370526479
    nsecs: 618319034
  frame_id: odom
pose: 
  pose: 
    position: 
      x: 0.0979321075308
      y: -0.0259189421082
      z: -0.00496891294622
    orientation: 
      x: 3.68249858201e-06
      y: -4.82461122176e-05
      z: 0.970055184809
      w: -0.242884614753
  covariance: [5.493486503712613e-07, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 5.493486503712613e-07, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0987278074026108e-06, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 10.877878440724317, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 10.877878440724317, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 5.493333772345698e-10]

And how I construct the odometry msg:

 void readAndSendVo()
  {
    tf::TransformListener lisVo;
    tf::StampedTransform transformVo;
    nav_msgs::Odometry vo;
    std ...

I'm trying to run Turtlebot using a fixed Kinect in the room, but I have some Tf issues in rviz. I cannot visualize robot Tf after base_footprint but I can see the link by:

$rosrun tf view_frames

$rosrun tf tf_echo /odom /base_footprint

> At time 1369922387.939
> - Translation: [0.000, 0.000, 0.000]
> - Rotation: in Quaternion [0.000, 0.000, 0.000, 1.000]
>             in RPY [0.000, -0.000, 0.000] At time 1369922388.974
> - Translation: [0.023, 0.001, 0.000]
> - Rotation: in Quaternion [0.000, 0.000, 0.070, 0.998]
>             in RPY [0.000, -0.000, 0.140] At time 1369922389.940
> - Translation: [0.023, 0.001, 0.000]
> - Rotation: in Quaternion [0.000, 0.000, 0.070, 0.998]
>             in RPY [0.000, -0.000, 0.140]

I can see some delay issues by tf_monitor: RESULTS: for all Frames

Frames: Frame: /ar_marker published by /pattern_reader Average Delay: 0.00499706 Max Delay: 0.0056474 Frame: /base_footprint published by /robot_pose_ekf Average Delay: 3415.33 Max Delay: 3415.36 Frame: /base_link published by /robot_state_publisher Average Delay: -0.486476 Max Delay: 0 Frame: /base_marker published by /baseMarker Average Delay: 0.000302851 Max Delay: 0.00233504 Frame: /camera_depth_frame published by /robot_state_publisher Average Delay: -0.486468 Max Delay: 0 Frame: /camera_depth_optical_frame published by /robot_state_publisher Average Delay: -0.486467 Max Delay: 0 Frame: /camera_link published by /robot_state_publisher Average Delay: -0.486478 Max Delay: 0 Frame: /camera_rgb_frame published by /robot_state_publisher Average Delay: -0.486466 Max Delay: 0 Frame: /camera_rgb_optical_frame published by /robot_state_publisher Average Delay: -0.486465 Max Delay: 0 Frame: /front_wheel_link published by /robot_state_publisher Average Delay: -0.486464 Max Delay: 0 Frame: /gyro_link published by /robot_state_publisher Average Delay: -0.486463 Max Delay: 0 Frame: /laser published by /robot_state_publisher Average Delay: -0.486462 Max Delay: 0 Frame: /left_cliff_sensor_link published by /robot_state_publisher Average Delay: -0.486475 Max Delay: 0 Frame: /left_wheel_link published by /robot_state_publisher Average Delay: 3414.33 Max Delay: 3414.34 Frame: /leftfront_cliff_sensor_link published by /robot_state_publisher Average Delay: -0.486473 Max Delay: 0 Frame: /odom published by /initialPose Average Delay: 0.000253598 Max Delay: 0.000583442 Frame: /openni_rgb_optical_frame published by /world_kinect_link Average Delay: -0.0998099 Max Delay: 0 Frame: /plate_0_link published by /robot_state_publisher Average Delay: -0.486461 Max Delay: 0 Frame: /plate_1_link published by /robot_state_publisher Average Delay: -0.48646 Max Delay: 0 Frame: /plate_2_link published by /robot_state_publisher Average Delay: -0.486459 Max Delay: 0 Frame: /plate_3_link published by /robot_state_publisher Average Delay: -0.486458 Max Delay: 0 Frame: /rear_wheel_link published by /robot_state_publisher Average Delay: -0.486457 Max Delay: 0 Frame: /right_cliff_sensor_link published by /robot_state_publisher Average Delay: -0.486472 Max Delay: 0 Frame: /right_wheel_link published by /robot_state_publisher Average Delay: 3414.33 Max Delay: 3414.34 Frame: /rightfront_cliff_sensor_link published by /robot_state_publisher Average Delay: -0.486471 Max Delay: 0 Frame: /spacer_0_link published by /robot_state_publisher Average Delay: -0.486456 Max Delay: 0 Frame: /spacer_1_link published by /robot_state_publisher Average Delay: -0.486455 Max Delay: 0 Frame: /spacer_2_link published by /robot_state_publisher Average Delay: -0.486454 Max Delay: 0 Frame: /spacer_3_link published by /robot_state_publisher Average Delay: -0.486453 Max Delay: 0 ...

Hi everybody,

Situation I use the package arm_navigation with ros-electric to control a arm robot with seven degree of freedom. I can send a articular or Cartesian goal, the robot moves well and avoid the object from urdf file like the table where is the robot. I use the "robot_name"_arm_navigation.launch and a robot simulation which receive joint state y publish TF of urdf with the good position.

Problematic I would use the kinect to add man body in the scene and more particularly at the collision map for the robot can avoid the body. I want to actualize it everytime. I tried three solutions. I want add the body as several cylinders which moves in "real time".

Solution 1: I followed the tutorial Adding known objects to the collision environment to add cylinder from kinect information. In this code I just add in the topic collision_object 10 cylinders to modeling the body.

Results 1: I succeeded to add correctly all cylinder for each body part. For example a arm it's modeling by a cylinder. The robot avoids well the body but just at the beginning of his movement. During the trajectory cylinders doesn't move and I can't check collisions.

Question 1: How actualize the collision map for see cylinders moves ?

Solution 2: It's a similar solution with the tutorial Adding Virtual Objects to the Planning Scene. The difference it's only that I don't use a topic to add objects but a service. I don't see others differences with the solution 1.

Result 2: With this solution cylinders can move everytime. There isn't the same problem of solution 1. But There is a more important problem with it. This solution works only when I choose a coordinate system already present in the urdf and doesn't work with a coodinate system generated by Kinect like right_arm_1. I explain me, when I add a cylinder, I add in a coordinate system(TF) and in this case it's more easy to add cylinder directly in coordinate system of the body. And with right_arm_1 or openni_depth_frame as coordinate system the cylinders doesn't fix in this coordinate system but works perfectly with coordinate system created from urdf file. I added a link between a urdf coordinate system and openni_depth_frame but it's like if there isn't this link.

Question 2: Why the environment_server doesn't understand the new TF of Kinect system ? How fix this ?

Solution 3: We thought to add the body man directly in the urdf and modify directly the tf with a publication in /tf.

Result3: it doesn't work because the simulation publish every time all urdf tf by /robot_st_pub. Only we can modify joint state. And it's more complicated to calculate joint state of the boy.

Question 3: It's possible to use urdf to add and modify mobil object ?

thank you for your help. if I should add details ask me.

EDIT1: Hi, Thanks for ... (more)