https://github.com/prbonn/online_place_recognition
Graph-based image sequences matching for the visual place recognition in changing environments.
https://github.com/prbonn/online_place_recognition
graph localization loop-closure online outdoor-localization recognition visual-place-recognition
Last synced: 4 months ago
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Graph-based image sequences matching for the visual place recognition in changing environments.
- Host: GitHub
- URL: https://github.com/prbonn/online_place_recognition
- Owner: PRBonn
- License: other
- Created: 2017-06-19T08:21:36.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2022-10-02T11:39:19.000Z (over 2 years ago)
- Last Synced: 2024-12-10T11:53:16.110Z (5 months ago)
- Topics: graph, localization, loop-closure, online, outdoor-localization, recognition, visual-place-recognition
- Language: C++
- Size: 1.95 MB
- Stars: 152
- Watchers: 9
- Forks: 40
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
- License: LICENSE.txt
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README
# Graph-based matching of image sequences
This project is not maintained. Please consider a newer version [image_sequence_localizer](https://github.com/ovysotska/image_sequence_localizer).
## 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](http://www.ipb.uni-bonn.de/pdfs/vysotska16ral-icra.pdf).
Checkout the video:
[](https://www.youtube.com/watch?v=l-hNk7Z4lSk&feature=youtu.be "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**](examples/readme.md).
#### 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](apps/feature_based_matching/readme.md).
For details on used feature descriptors please refer to [feature description](apps/feature_based_matching/readme.md#feature-format).
**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](apps/create_cost_matrix/readme.md).
An example on how to run the matching procedure in this mode can be found [cost matrix matching example](apps/cost_matrix_based_matching/readme.md).
**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](src/features/readme.md).
## 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.