Graph-based image sequences matching for the visual place recognition in changing environments.
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readme.md

Graph-based matching of image sequences

What does this code do?

Given two sequences of images represented by the descriptors, the code constructs a data association graph and performs a search within this graph, so that for every query image, the code computes a matching hypothesis to an image in a database sequence as well as matching hypothesis for the previous images.

The matching procedure can be perfomed in two modes --- feature based and cost matrix based mode.

For more theoretical details, please refer to our paper Lazy data association for image sequence matching under substantial appearance changes.

Checkout the video:

Matching example video

Installation guide

Prerequisites

  • Yaml-cpp (requires BOOST till now): sudo apt-get install libyaml-cpp-dev
  • OpenCV: sudo apt-get install libopencv-dev
  • Qt5: sudo apt-get install qt5-default
  • (optional) Doxygen (generate documentation): sudo apt-get install doxygen

For the OSX install, you may need to run the following commands:

  • brew install yaml-cpp
  • brew install opencv
  • brew install doxygen
  • brew install qt5
  • export CMAKE_PREFIX_PATH=/usr/local/Cellar/qt/[YOUR VERSION] For example eg. export CMAKE_PREFIX_PATH=/usr/local/Cellar/qt/5.8.0_2

Build

To build the project, run the following commands from the main directory:

  • mkdir build
  • cd build
  • cmake ..
  • make -j4

Additionally, you should be able to generate documentation as follows:

  • cd doc
  • doxygen online_place_recognition.conf

To access the documentation run firefox html/index.html.

What do I need to run this code?

  • Precomputed image descriptors or cost matrix
  • Configuration file

An example of how to run the code please see RUN EXAMPLES.

Feature based matching

In this mode, the program operates using precomputed image descriptors. To run the matching procedure you need to provide the feature files. An example of how to run the code can be found feature based matching example.

For details on used feature descriptors please refer to feature description.

Note: In this mode, individual features will be loaded and matched on demand. In order to be able to deal with dramatic visual changes, we typically operate with high-dimensional features and the matching procedure can take quite a long time--depending on the size and the complexity of the sequences.

Cost matrix based matching

For this mode, we require the cost matrix between two sequences to be given/pre-computed. To compute the matching matrix, please see the following estimating of a cost matrix example.

An example on how to run the matching procedure in this mode can be found cost matrix matching example.

Note: This method may be used if you have rather small sequences (up to 1000 images). For bigger sequences, you may run into memory issues since the programs has to store a quite big matrix.

Adapting the code for custom features

This framework can be adapted to matching features of the different type --- your own features. To use the graph matching strategy with your own features see the following description.

Related publication

Please cite the related publication, if you use the code:

@article{vysotska2016lazy, 
  title     = {Lazy Data Association for Image Sequences Matching Under Substantial Appearance Changes},
  author    = {Vysotska, Olga and Stachniss, Cyrill},
  year      = {2016},
  publisher = {IEEE Robotics and Automation Letters}
  number    = {1},
  pages     = {1-8},
  volume    = {1},
  doi       = {10.1109/LRA.2015.2512936}
}

Troubleshooting

In case the code is not working for you or you experience some code related problems, please consider openning an issue.