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Orthogonal Plane-based Visual Odometry (OPVO)

This package provides a Matlab implementation of BMVC 2017 paper: "Visual Odometry with Drift-Free Rotation Estimation Using Indoor Scene Regularities" for the purpose of research and study only.

OPVO

1. License

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

if you use OPVO in an academic work, please cite:

@inproceedings{kim2017visual,
  author = {Kim, Pyojin and Coltin, Brian and Kim, H Jin},
  title = {Visual Odometry with Drift-Free Rotation Estimation Using Indoor Scene Regularities},
  year = {2017},
  booktitle = {British Machine Vision Conference (BMVC)},
 }

2. Prerequisites

This package depends on mexopencv library for keypoint processing, KLT tracking, and translation estimation. cv.* functions in this package cannot run without mexopencv install in Matlab environment. Please, build mexopencv in your OS first, and then run this package.

3. Usage

  • Download the ICL-NUIM dataset from https://www.doc.ic.ac.uk/~ahanda/VaFRIC/iclnuim.html, 'of kt3' is recommanded.

  • Or, Use the ICL-NUIMdataset/of_kt3/ included in this package.

  • Define 'datasetPath' correctly in your directory at setupParams_ICL_NUIM.m file.

  • Run OPVO_core/main_script_ICL_NUIM.m which will give you the 3D motion estimation result. Enjoy! :)

4. Publications

The approach is descirbed and used in the following publications:

  • Linear RGB-D SLAM for Planar Environments (Pyojin Kim, Brian Coltin, and H. Jin Kim), ECCV 2018.

  • Indoor RGB-D Compass from a Single Line and Plane (Pyojin Kim, Brian Coltin, and H. Jin Kim), CVPR 2018.

  • Low-Drift Visual Odometry in Structured Environments by Decoupling Rotational and Translational Motion (Pyojin Kim, Brian Coltin, and H. Jin Kim), ICRA 2018.

You can find more related papers at http://pyojinkim.me/_pages/pub/index.html.