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https://github.com/fabianschenk/RESLAM

RESLAM: A real-time robust edge-based SLAM system
https://github.com/fabianschenk/RESLAM

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RESLAM: A real-time robust edge-based SLAM system

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# RESLAM: A real-time robust edge-based SLAM system



**Please note that RESLAM is a research project and its code is released without any warranty. RESLAM will most likely not be developed any further**



In this work, we present **RESLAM**, a robust edge-based SLAM system for RGBD sensors. Edges are more stable under varying lighting conditions than raw intensity values, which leads to higher accuracy and robustness in scenes, where feature- or photoconsistency-based approaches often fail. The results show that our method performs best in terms of trajectory accuracy for most of the sequences indicating that edges are suitable for a multitude of scenes.



## If you use this work, please cite any of the following publications:

* **RESLAM: A real-time robust edge-based SLAM system**, Schenk Fabian, Fraundorfer Friedrich, ICRA 2019, [pdf](https://github.com/fabianschenk/fabianschenk.github.io/raw/master/files/schenk_icra_2019.pdf)

* **Combining Edge Images and Depth Maps for Robust Visual Odometry**, Schenk Fabian, Fraundorfer Friedrich, BMVC 2017, [pdf](https://github.com/fabianschenk/fabianschenk.github.io/raw/master/files/schenk_bmvc_2018.pdf),[video](https://youtu.be/uj3rRyqSEnQ)

* **Robust Edge-based Visual Odometry using Machine-Learned Edges**, Schenk Fabian, Fraundorfer Friedrich, IROS 2017, [pdf](https://github.com/fabianschenk/fabianschenk.github.io/raw/master/files/schenk_iros_2017.pdf), [video](https://youtu.be/PUTV9vsdpbA)



## License

RESLAM is licensed under the [GNU General Public License Version 3 (GPLv3)](http://www.gnu.org/licenses/gpl.html).



If you want to use this software commercially, please contact us.



## Building the framework

So far, the framework has only been built and tested on the following system.

### Requirements

* [Ubuntu 16.04, 15.10, 17.04](https://www.ubuntu.com/)

* [OpenCV > 3](http://opencv.org/)

* [Eigen > 3.3](http://eigen.tuxfamily.org/index.php?title=Main_Page)

* [Ceres >= 1.13](http://ceres-solver.org/installation.html)



[Sophus](https://github.com/strasdat/Sophus) is now part of this repository (in thirdparty/Sophus).



Building on Windows and backwards compatibility might be added in the future.



### Optional

Set the optional packages in the cmake-gui

* [Pangolin](https://github.com/stevenlovegrove/Pangolin)  (for graphical viewer)



### Build commands

```bash

git clone https://github.com/fabianschenk/RESLAM

cd RESLAM

mkdir build

cd build

cmake . ..

make -j

```



### Known Issues

#### Segmentation Fault with Ceres/Eigen [#2](https://github.com/fabianschenk/RESLAM/issues/2), and [#3](https://github.com/fabianschenk/RESLAM/issues/3).

Some people report a problem with Ceres/Eigen.

Please, have a look at [#2](https://github.com/fabianschenk/RESLAM/issues/2), and [#3](https://github.com/fabianschenk/RESLAM/issues/3).

Make sure that you have the latest (stable) [Eigen version 3.3.X](http://eigen.tuxfamily.org/index.php?title=Main_Page) and that it matches the one used by Ceres.



#### Segmentation fault after repeated tracking losses [#3](https://github.com/fabianschenk/RESLAM/issues/3)

In some sequences, e.g. `freiburg2_large_with_loop`, there are depth maps containing mostly invalid values.

The problem is that the Kinect and most other RGBD sensors cannot reconstruct surfaces far away from sensor (around > 6 m) due to the small baseline of the sensor.

In such cases, RESLAM does not work and might fail with a segmentation fault after repeated tracking losses. This issue will hopefully be fixed in the future.



## How to reproduce the results from the paper



**If you enable multi-threading, results might differ a bit since float additions are not executed in the same order during each run!**



### [TUM dataset](https://vision.in.tum.de/data/datasets/rgbd-dataset)

Download the sequence you want to test and specify the "associate.txt" file in the dataset_tumX.yaml settings file.



To generate an "associate.txt" file, first download the "associate.py" script from [TUM RGBD Tools](https://svncvpr.in.tum.de/cvpr-ros-pkg/trunk/rgbd_benchmark/rgbd_benchmark_tools/src/rgbd_benchmark_tools/) and then run

```bash

python associate.py DATASET_XXX/rgb.txt DATASET_XXX/depth.txt > associate.txt

```

in the folder, where your dataset is.

 

In the "RESLAM" directory:

```bash

build/RESLAM config_files/reslam_settings.yaml config_files/dataset_tum1.yaml

```

For evaluation of the absolute trajectory error (ATE) and relative pose error (RPE) download the corresponding scripts from [TUM RGBD Tools](https://svncvpr.in.tum.de/cvpr-ros-pkg/trunk/rgbd_benchmark/rgbd_benchmark_tools/src/rgbd_benchmark_tools/).



 

## Supported Sensors



Support for other sensors such as Orbbec Astra Pro and Intel RealSense can be adapted from [REVO](https://github.com/fabianschenk/REVO).