# stvo-pl-debug

**Repository Path**: NiShuiDuLiu/stvo-pl-debug

## Basic Information

- **Project Name**: stvo-pl-debug
- **Description**: No description available
- **Primary Language**: C++
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-05-07
- **Last Updated**: 2021-05-07

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# PL-StVO #

This code contains an algorithm to compute stereo visual odometry by using both point and line segment features.

**Authors:** [Ruben Gomez-Ojeda](http://mapir.isa.uma.es/mapirwebsite/index.php/people/164-ruben-gomez) 
and [David Zuñiga-Noël](http://mapir.isa.uma.es/mapirwebsite/index.php/people/270)
and [Javier Gonzalez-Jimenez](http://mapir.isa.uma.es/mapirwebsite/index.php/people/95-javier-gonzalez-jimenez)

**Related publication:** [*Robust Stereo Visual Odometry through a Probabilistic Combination of Points and Line Segments*](http://mapir.isa.uma.es/mapirwebsite/index.php/people/164-ruben-gomez)

If you use PL-StVO in your research work, please cite:

    @InProceedings{Gomez2015,
      Title                    = {Robust Stereo Visual Odometry through a Probabilistic Combination of Points and Line Segments},
      Author                   = {Gomez-Ojeda, Ruben and Gonzalez-Jimenez, Javier},
      Booktitle                = {Robotics and Automation (ICRA), 2016 IEEE International Conference on},
      Year                     = {2016},
      Publisher                = {IEEE}
    }

The provided code is published under the General Public License Version 3 (GPL v3). More information can be found in the "GPU_LICENSE.txt" also included in the repository.

Please do not hesitate to contact the authors if you have any further questions.


## 1. Prerequisites and dependencies

### OpenCV 3.x.x
It can be easily found at http://opencv.org. 

### Eigen3
http://eigen.tuxfamily.org

### Boost
Installation on Ubuntu 16.04:
```
sudo apt-get install libboost-dev
```

### YAML
Installation on Ubuntu 16.04:
```
sudo apt install libyaml-cpp-dev
```

### MRPT
In case of using the provided representation class. 
Download and install instructions can be found at: http://www.mrpt.org/

#### Known Issues:
If working with the most recent versions of the MRPT library you might find some issues due to hard refactoring, for which we recommend to use this version instead (the last one we tested):
```
https://github.com/MRPT/mrpt/tree/0c3d605c3cbf5f2ffb8137089e43ebdae5a55de3
```

### Line Descriptor
We have modified the [*line_descriptor*](https://github.com/opencv/opencv_contrib/tree/master/modules/line_descriptor) module from the [OpenCV/contrib](https://github.com/opencv/opencv_contrib) library (both BSD) which is included in the *3rdparty* folder.




## 2. Configuration and generation
A CMakeLists.txt file is included to detect external dependencies and generate the project.

The project builds "imagesStVO", a customizable application where the user must introduce the inputs to the SVO algorithm, and then process the provided output. 



## 3. Usage

### Datasets configuration
We employ an environment variable, *${DATASETS_DIR}*, pointing the directory that contains our datasets. Each sequence from each dataset must contain in its root folder a file named *dataset_params.yaml*, that indicates at least the camera model and the subfolders with the left and right images. We provide dataset parameters files for several datasets and cameras with the format *xxxx_params.yaml*.

### Configuration files
For running VO we can load the default parameters file or employ the *config_xxxx.yaml* files provided for every dataset.

### VO Application
Usage: ./imagesStVO <dataset_name> [options]
Options:
	-c Config file
	-o Offset (number of frames to skip in the dataset directory
	-n Number of frames to process the sequence
	-s Parameter to skip s-1 frames (default 1)

A full command would be:

./imagesStVO kitti/00 -c ../config/config_kitti.yaml -o 100 -s 2 -n 1000

where we are processing the sequence 00 from the KITTI dataset (in our dataset folders) with the custom config file, with an offset *-c* allowing to skip the first 100 images, a parameter *-s* to consider only one every 2 images, and a parameter *-n* to only consider 1000 input pairs.