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https://github.com/srv/viso2

A ROS wrapper for libviso2, a library for visual odometry
https://github.com/srv/viso2

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A ROS wrapper for libviso2, a library for visual odometry

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viso2

==========

ROS Stack containing a wrapper for libviso2, a visual odometry library. 

http://www.ros.org/wiki/viso2 for the list of contained packages.

***



## Omnidirectional version



* This package contains an additional version of the original Libviso2 that works with a **monocular omnidirectional camera system**.



### Dependencies



* The only additional dependency of this version is the camera calibration toolbox [Ocamcalib](https://sites.google.com/site/scarabotix/ocamcalib-toolbox) for Matlab.

  - Instead of using a	 normal pinhole camera calibration model, the method uses the unified camera model proposed by Davide Scaramuzza.

  - Download the toolbox from [here](https://sites.google.com/site/scarabotix/ocamcalib-toolbox/ocamcalib-toolbox-download-page), put it in your Matlab workspace and execute the `ocam_calib` command to run the application.

  - After calibrating the camera save the generated text file that will be used by the method.



### How to execute



* To execute omnidirectional Libviso2 make sure you have a camera node publishing over ROS.

* After that you can use the demo launch file that we provide in the following way:

```

roslaunch viso2_ros demo.launch

```

* Make sure that first you set the **file calibration path** correctly by changing the `calib_path` parameter.

* Also you can edit/add the default parameters that Libviso2 use (don't forget that camera height and pitch are mandatory to scale calculation).



### Performance test



* If you want to check the performance of the method before using it in your own system you can use the bag file that we provide [here](https://drive.google.com/file/d/1FfBOMPrdk-uqLeNm3NNGXJQxdkKqgUfI/view?usp=sharing).



##### Available topics on the bag file

1. `perspective_cam/image/compressed` contains a perspective camera sequence that can be used by the default monocular Libviso2 version to benchmark.

2. `fisheye_cam/image/compressed` contains the same sequence captured by a fisheye camera.

3. `slam_out_pose` contains the camera motion estimation computed by Hector SLAM method.

4. `/husky_velocity_controller/odom` constains conventional odometry motion estimation.

5. `/fix` constains GPS data.

6. `/map` contains a map of the environment designed using a laser.

7. (...) and others.



##### Running the test sequences



* To run both test sequences you can execute the following commands after setting the proper paths for the bag file and the calibration file.

```

roslaunch viso2_ros perspective.launch

roslaunch viso2_ros fisheye.launch

```

**NOTE:** We provide the **camera calibration file** for the fisheye camera ('data/calib_results.txt'). You have to set the correct path to the rosbag file in both launch files and the calibration text file in the fisheye.launch. 

We also provide a **rviz configuration file** to easy visualization of the results.



## Acknowledges



This work is co-financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 under the PORTUGAL 2020 Partnership Agreement, and through the Portuguese National Innovation Agency (ANI) as a part of project «ROMOVI: POCI-01-0247-FEDER-017945»".