RESLAM: A real-time robust edge-based SLAM system
Please note that the code is still in the testing phase and I plan to update some parts soon!
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
- Combining Edge Images and Depth Maps for Robust Visual Odometry, Schenk Fabian, Fraundorfer Friedrich, BMVC 2017, pdf,video
- Robust Edge-based Visual Odometry using Machine-Learned Edges, Schenk Fabian, Fraundorfer Friedrich, IROS 2017, pdf, video
RESLAM is licensed under the GNU General Public License Version 3 (GPLv3).
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.
Sophus is now part of this repository (in thirdparty/Sophus).
Building on Windows and backwards compatibility might be added in the future.
Set the optional packages in the cmake-gui
- Pangolin (for graphical viewer)
git clone https://github.com/fabianschenk/RESLAM cd RESLAM mkdir build cd build cmake . .. make -j
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!
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 and then run
python associate.py DATASET_XXX/rgb.txt DATASET_XXX/depth.txt > associate.txt
in the folder, where your dataset is.
In the "RESLAM" directory:
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.
Support for other sensors such as Orbbec Astra Pro and Intel RealSense will follow in the next weeks.