Fast Plane Extraction Using Agglomerative Hierarchical Clustering (AHC)

Legal Remarks

Copyright 2014 Mitsubishi Electric Research Laboratories All Rights Reserved.

Permission to use, copy and modify this software and its documentation without fee for educational, research and non-profit purposes, is hereby granted, provided that the above copyright notice, this paragraph, and the following three paragraphs appear in all copies.

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Overview

This source code package contains our C++ implementation of the AHC based fast plane extraction for organized point cloud (point cloud that can be indexed as an image). There are three folders in this package:

• include

  Our C++ implementation of the algorithm with dependencies on OpenCV and shared_ptr (from C++11 or Boost).

• cpp

  Two example C++ console applications using our algorithm to extract planes from Kinect-like point cloud (depends on PCL), with a CMake script to help generating project files.

• matlab

  A matlab interface (fitAHCPlane.m) through MEX for using our algorithm in matlab. We also provide a wrapper class Kinect.m and kinect_ahc.m to do real-time plane extraction in matlab, partially depends on a 3rd-party toolbox Kinect_Matlab.

If you use this package, please cite our ICRA 2014 paper:

Feng, C., Taguchi, Y., and Kamat, V. R. (2014). "Fast Plane Extraction in Organized Point Clouds Using Agglomerative Hierarchical Clustering." Proceedings of the IEEE International Conference on Robotics and Automation, Hong Kong, China, 6218-6225.

Version

1.0

Installation

C++ example

1. Install OpenCV, Boost and PCL (If you install PCL using their all-in-one installer, you directly get Boost installed as well).

2. Generate project file using CMake under either Windows or Linux.

3. Compile.

4. Run the compiled process: plane_fitter (first connect a Kinect to your computer!) or plane_fitter_pcd (first modify plane_fitter_pcd.ini accordingly!).

5. Enjoy!

Matlab example

1. In matlab:

   cd WHERE_YOU_EXTRACT_THE_PACKAGE/matlab/mex

2. Run makefile.m

3. Select the directories for OpenCV_Include, OpenCV_Lib, and Boost_Include respectively

4. If everything compiles smoothly:

   cd ..

5. Load a single frame we've prepared for you in matlab by:

   load frame

6. Run our algorithm on the point cloud:

   frame.mbs=fitAHCPlane(frame.xyz);
   viewSeg(frame.mbs,640,480)

7. Enjoy!

8. If you want to play with the kinect_ahc.m with a Kinect, install Kinect_Matlab first.

Contact

Chen Feng

Feel free to email any bugs or suggestions to help us improve the code. Thank you!