Documentation Status

vision_visp: visp | visp_auto_tracker | visp_bridge | visp_camera_calibration | visp_hand2eye_calibration | visp_tracker

Package Summary

Documented

visp_auto_tracker wraps model-based trackers provided by ViSP visual servoing library into a ROS package. The tracked object should have a QRcode of Flash code pattern. Based on the pattern, the object is automaticaly detected. The detection allows then to initialise the model-based trackers. When lost of tracking achieves a new detection is performed that will be used to re-initialize the tracker. This computer vision algorithm computes the pose (i.e. position and orientation) of an object in an image. It is fast enough to allow object online tracking using a camera.

vision_visp: visp | visp_auto_tracker | visp_bridge | visp_camera_calibration | visp_hand2eye_calibration | visp_tracker

Package Summary

Documented

visp_auto_tracker wraps model-based trackers provided by ViSP visual servoing library into a ROS package. The tracked object should have a QRcode of Flash code pattern. Based on the pattern, the object is automaticaly detected. The detection allows then to initialise the model-based trackers. When lost of tracking achieves a new detection is performed that will be used to re-initialize the tracker. This computer vision algorithm computes the pose (i.e. position and orientation) of an object in an image. It is fast enough to allow object online tracking using a camera.

vision_visp: visp_auto_tracker | visp_bridge | visp_camera_calibration | visp_hand2eye_calibration | visp_tracker

Package Summary

Released Continuous integration Documented

Online automated pattern-based object tracker relying on visual servoing. visp_auto_tracker wraps model-based trackers provided by ViSP visual servoing library into a ROS package. The tracked object should have a QRcode of Flash code pattern. Based on the pattern, the object is automaticaly detected. The detection allows then to initialise the model-based trackers. When lost of tracking achieves a new detection is performed that will be used to re-initialize the tracker. This computer vision algorithm computes the pose (i.e. position and orientation) of an object in an image. It is fast enough to allow object online tracking using a camera.

vision_visp: visp_auto_tracker | visp_bridge | visp_camera_calibration | visp_hand2eye_calibration | visp_tracker

Package Summary

Released Continuous integration Documented

Online automated pattern-based object tracker relying on visual servoing. visp_auto_tracker wraps model-based trackers provided by ViSP visual servoing library into a ROS package. The tracked object should have a QRcode of Flash code pattern. Based on the pattern, the object is automaticaly detected. The detection allows then to initialise the model-based trackers. When lost of tracking achieves a new detection is performed that will be used to re-initialize the tracker. This computer vision algorithm computes the pose (i.e. position and orientation) of an object in an image. It is fast enough to allow object online tracking using a camera.

vision_visp: visp_auto_tracker | visp_bridge | visp_camera_calibration | visp_hand2eye_calibration | visp_tracker

Package Summary

Released Continuous integration Documented

Online automated pattern-based object tracker relying on visual servoing. visp_auto_tracker wraps model-based trackers provided by ViSP visual servoing library into a ROS package. The tracked object should have a QRcode of Flash code pattern. Based on the pattern, the object is automaticaly detected. The detection allows then to initialise the model-based trackers. When lost of tracking achieves a new detection is performed that will be used to re-initialize the tracker. This computer vision algorithm computes the pose (i.e. position and orientation) of an object in an image. It is fast enough to allow object online tracking using a camera.

vision_visp: visp_auto_tracker | visp_bridge | visp_camera_calibration | visp_hand2eye_calibration | visp_tracker

Package Summary

Released Continuous integration Documented

Online automated pattern-based object tracker relying on visual servoing. visp_auto_tracker wraps model-based trackers provided by ViSP visual servoing library into a ROS package. The tracked object should have a QRcode of Flash code pattern. Based on the pattern, the object is automaticaly detected. The detection allows then to initialise the model-based trackers. When lost of tracking achieves a new detection is performed that will be used to re-initialize the tracker. This computer vision algorithm computes the pose (i.e. position and orientation) of an object in an image. It is fast enough to allow object online tracking using a camera.

vision_visp: visp_auto_tracker | visp_bridge | visp_camera_calibration | visp_hand2eye_calibration | visp_tracker

Package Summary

Released Continuous integration Documented

Online automated pattern-based object tracker relying on visual servoing. visp_auto_tracker wraps model-based trackers provided by ViSP visual servoing library into a ROS package. The tracked object should have a QRcode of Flash code pattern. Based on the pattern, the object is automaticaly detected. The detection allows then to initialise the model-based trackers. When lost of tracking achieves a new detection is performed that will be used to re-initialize the tracker. This computer vision algorithm computes the pose (i.e. position and orientation) of an object in an image. It is fast enough to allow object online tracking using a camera.

vision_visp: visp_auto_tracker | visp_bridge | visp_camera_calibration | visp_hand2eye_calibration | visp_tracker

Package Summary

Released Continuous integration Documented

Online automated pattern-based object tracker relying on visual servoing. visp_auto_tracker wraps model-based trackers provided by ViSP visual servoing library into a ROS package. The tracked object should have a QRcode of Flash code pattern. Based on the pattern, the object is automaticaly detected. The detection allows then to initialise the model-based trackers. When lost of tracking achieves a new detection is performed that will be used to re-initialize the tracker. This computer vision algorithm computes the pose (i.e. position and orientation) of an object in an image. It is fast enough to allow object online tracking using a camera.

Overview

This package wraps an automated pattern barcode based tracker using ViSP library. The tracker estimates the pattern position and orientation with respect to the camera. It requires the pattern 3d model and a configuration file.

The algorithm allows first to detect automatically the barcode using one of the following detectors:

  • QR-code detection
  • flashcode detection

Then from the location of the 4 barcode corners it computes an initial pose using a PnP algorithm. This pose allows to initialize the model based tracker that is dedicated to track the two squares defining the black area arround the barcode. For the tracking we use an hybrid approach that considers moving-edges and keypoint features that are mainly located on the barcode. Finally, the tracker is also able to detect loss of tracking and recover from it entering in a new barcode detection and localization stage.

The package is composed of one node called visp_auto_tracker. This node tries to track the object as fast as possible. The viewer coming with visp_tracker package can be used to monitor the tracking result.

The next video shows how to track a specific pattern textured with a QRcode. ViSP model-based tracker detects when it fails and recover the object position thanks to QRcode detection.

Reference

Calibration Requirements

Currently the visp_auto_tracker package requires calibration information from a camera_info topic. To this end visp_camera_calibration package can be used.

Features

The package purpose is to provide the 3D pose of an object in a sequence of images. The object has to be textured with a pattern on one face. The pattern has to be included into a white box, itself included in a black box.

This is an example of a valid QR-code pattern that can be downloaded here.

template-qr-code-small.png

This is an example of a valid flash-code pattern that can be downloaded here.

template-flash-code-small.png

Installation

visp_auto_tracker is part of vision_visp stack.

  • To install visp_auto_tracker package run

    sudo apt-get install ros-$ROS_DISTRO-visp-auto-tracker
  • Or to install the complete stack run

    sudo apt-get install ros-$ROS_DISTRO-vision-visp

Examples

You can run visp_auto_tracker on a pre-recorded bag file that comes with the package, or on a live video from a camera.

Pre-recorded example

To run visp_auto_tracker on a pre-recorded image sequence, just run:

roslaunch launch/tutorial.launch

The pattern used in this example can be downloaded here.

Live video examples

You have a ready-to-use roslaunch file in launch/tracklive_firewire.launch. This works with a firewire (1394) camera. If you have an usb camera (like a webcam) you can use launch/tracklive_usb.launch launch file.

You can launch with the following command line:

roslaunch launch/tracklive_firewire.launch

Config file

visp_auto_tracker centralises most of its parameters inside a configuration file following the boost::program_options default format.

The basic configuration file would look like this:

#set the detector type: "zbar" to detect QR code, "dmtx" to detect flashcode
detector-type= zbar
#enable recovery mode when the tracker fails
ad-hoc-recovery= 1

#point 1
flashcode-coordinates= -0.024
flashcode-coordinates= -0.024
flashcode-coordinates= 0.000
#point 2
flashcode-coordinates= 0.024
flashcode-coordinates= -0.024
flashcode-coordinates= 0.000
#point 3
flashcode-coordinates= 0.024
flashcode-coordinates= 0.024
flashcode-coordinates= 0.000
#point 4
flashcode-coordinates= -0.024
flashcode-coordinates= 0.024
flashcode-coordinates= 0.000

#point 1
inner-coordinates= -0.038
inner-coordinates= -0.038
inner-coordinates= 0.000
#point 2
inner-coordinates= 0.038
inner-coordinates= -0.038
inner-coordinates= 0.000
#point 3
inner-coordinates= 0.038
inner-coordinates= 0.038
inner-coordinates= 0.000
#point 4
inner-coordinates= -0.038
inner-coordinates= 0.038
inner-coordinates= 0.000

#point 1
outer-coordinates= -0.0765
outer-coordinates= -0.0765
outer-coordinates= 0.000
#point 2
outer-coordinates= 0.0765
outer-coordinates= -0.0765
outer-coordinates= 0.000
#point 3
outer-coordinates= 0.0765
outer-coordinates= 0.0765
outer-coordinates= 0.000
#point 4
outer-coordinates= -0.0765
outer-coordinates= 0.0765
outer-coordinates= 0.000

Common parameters

detector-type

The following detectors are supported

  • detector-type= zbar: uses libzbar to detect QRcodes

  • detector-type= dmtx: uses libdmtx to detect flashcodes

flashcode-coordinates

3D-coordinates in meters of the box delimiting the pattern (QRcode or flashcode).

inner-coordinates

3D-coordinates in meters of the white box containing the pattern.

outer-coordinates

3D-coordinates in meters of the black box containing the pattern.

ad-hoc-recovery

When set (tracker-type= 1) this parameter activates the tracking lost detection and recovery using flashcode-coordinates, inner-coordinates and outer-coordinates point coordinates.

Tracker states

The tracker is a state machine whose states vary during the tracking process. The process includes tracking, loss and recovery. These are the states used:

  • Waiting For Input (id: 0) : Not detecting any pattern, just recieving images
  • Detect Flashcode (id: 1) : Pattern detected.
  • Detect Model (id: 2) : Model successfully initialized (from wrl & xml files).

  • Track Model (id: 3) : Tracking model.
  • Re Detect Flashcode (id: 4) : Detecting pattern in a small region around where the pattern was last seen.
  • Detect Flash code (id: 5) : Detecting pattern in a the whole frame.

Viewer

When you track a model, you probably want a visual feedback. You can get one by connecting rviz to the outputed /object_position topic. visp_auto_tracker does not have a dedicated viewer. It can use the viewer provided with visp_tracker package, specifically visp_tracker/visp_tracker_viewer node.

Without connecting another node, you can also open a debug graphical output directly from the visp_auto_tracker node by setting the debug_display parameter.

The following figure shows the debug output (left) next to the external visp_tracker/viewer (right) in the case of the hybrid model-based tracker with QR-code initialisation:

tracker_viewer-small.png

Nodes

visp_auto_tracker

Subscribes to a camera and publishes pose.

Subscribed Topics

image_raw (sensor_msgs/Image)
  • The image topic. Should be remapped to the name of the real image topic.
camera_info (sensor_msgs/CameraInfo)
  • The camera parameters.

Published Topics

object_position (geometry_msgs/PoseStamped)
  • 3D pose of the model.
object_position_covariance (geometry_msgs/PoseWithCovarianceStamped)
  • 3D pose of the model. The covariance part is unused
status (std_msgs/Int8)
  • Status of the automatic tracker. See tracker states for more information.
moving_edge_sites (visp_tracker/MovingEdgeSites)
  • Moving edge sites information (stamped). For debugging/monitoring purpose.
klt_points_positions (visp_tracker/KltPoints)
  • Position and id of the keypoints (stamped). For debugging/monitoring purpose.

Parameters

model_path (string)
  • path to where the models are stored.
model_name (string)
  • model name. Name of the cfg, wrl and xml files. If model_path is /path/ and model_name is model then /path/model.wrl, /path/model.xml and /path/model.cfg will be loaded. The content of the cfg file is described in "Config file" section.
debug_display (boolean)
  • display debug information about tracking

Report a bug

Use GitHub to report a bug or submit an enhancement.

Wiki: visp_auto_tracker (last edited 2016-08-16 10:08:22 by FabienSpindler)