https://github.com/tum-vision/dvo

Dense Visual Odometry
https://github.com/tum-vision/dvo

Last synced: 27 days ago
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Dense Visual Odometry

• Host: GitHub
• URL: https://github.com/tum-vision/dvo
• Owner: tum-vision
• Created: 2012-12-10T09:45:42.000Z (over 11 years ago)
• Default Branch: fuerte
• Last Pushed: 2013-07-11T14:04:47.000Z (almost 11 years ago)
• Last Synced: 2024-03-26T21:34:16.438Z (3 months ago)
• Language: C++
• Homepage: https://vision.in.tum.de/data/software/dvo
• Size: 270 KB
• Stars: 151
• Watchers: 24
• Forks: 77
• Open Issues: 15
• Metadata Files:
  • Readme: README.md

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• Awesome-SLAM - DVO

README

        
# Dense Visual Odometry (dvo)

These packages provide an implementation of the rigid body motion estimation of an RGB-D camera from consecutive images.

 *  **dvo_core**

    

    Core implementation of the motion estimation algorithm. 

    

 *  **dvo_ros**

    

    Integration of *dvo_core* with ROS.

    

 *  **dvo_benchmark**

    

    Integration of *dvo_core* with TUM RGB-D benchmark, see http://vision.in.tum.de/data/datasets/rgbd-dataset.

    

 *  **sophus**

    

    ROS package wrapper for Hauke Strasdat's Sophus library, see https://github.com/strasdat/Sophus.

    

## Installation

Checkout the branch for your ROS version into a folder in your `ROS_PACKAGE_PATH` and build the packages with `rosmake`.

 *  ROS Fuerte:

    

    ```bash

    git clone -b fuerte git://github.com/tum-vision/dvo.git

    rosmake dvo_core dvo_ros dvo_benchmark

    ```

 *  ROS Electric:

    

    You need to install `perception_pcl_unstable` with PCL version 1.5+.

    

    ```bash

    git clone -b electric git://github.com/tum-vision/dvo.git

    rosmake dvo_core dvo_ros dvo_benchmark

    ```

## Usage

Estimating the camera trajectory from an RGB-D image stream:

 *  Start the OpenNI camera driver: `roslaunch openni_launch openni.launch`

 *  Start the dvo *camera_tracker* node: `rosrun dvo_ros camera_tracker`

 *  Start dynamic_reconfigure GUI

    *   In `/camera/driver` enable *depth_registration* on

    *   In `/camera_tracker` enable *reconstruction*, *use_weighting*, *run_dense_tracking*, and *use_dense_tracking_estimate*

If everything works, the stdout of the *camera_tracker* node should show `[ WARN] [1355131430.132265592]: RGB image size has changed, resetting tracker!` and the camera pose is published on the `/rgbd/pose` topic. You can restart the camera motion estimation by disabling and enabling the *run_dense_tracking* option.

For visualization:

 *  Start RVIZ

 *  Set the *Target Frame* to `/world`

 *  Add an *Interactive Marker* display and set its *Update Topic* to `/dvo_vis/update`

 *  Add a *PointCloud2* display and set its *Topic* to `/dvo_vis/cloud`

The red camera shows the current camera position. The blue camera displays the initial camera position.

## Publications

The following publications describe the approach:

 *   **Robust Odometry Estimation for RGB-D Cameras** (C. Kerl, J. Sturm, D. Cremers), In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), 2013

 *   **Real-Time Visual Odometry from Dense RGB-D Images** (F. Steinbruecker, J. Sturm, D. Cremers), In Workshop on Live Dense Reconstruction with Moving Cameras at the Intl. Conf. on Computer Vision (ICCV), 2011.

## License

The packages *dvo_core*, *dvo_ros*, and *dvo_benchmark* are licensed under the GNU General Public License Version 3 (GPLv3), see http://www.gnu.org/licenses/gpl.html.

The package *sophus* is licensed under the MIT License, see http://opensource.org/licenses/MIT.