SLAM2REF: Advancing Long-Term Mapping with 3D LiDAR and Reference Map Integration for Precise 6-DoF Trajectory Estimation and Map Extension

[Paper] [Data]

What is SLAM2REF?

Using Pose-graph Multi-Session Anchoring with a reference map or with another session, this project allows the alignment and correction of LiDAR-based SLAM sessions, allowing precise 6-DoF pose retrieval and map extension.

• This project is an extension of LT-SLAM, which implements a custom GTSAM factor for anchoring (see BetweenFactorWithAnchoring.h). However, this project is completely ROS-independent.
• Moreover, we have implemented a novel Indoor Scan Context Descriptor for fast place recognition.
• Also, a novel YawGICP algorithm for robust point cloud registration with varying mostly yaw angles.
• SLAM2REF additionally allows the retrieval of 6-DoF poses with an accuracy of up to 3 cm given an accurate TLS point cloud as a reference map (this map should be accurate, at least regarding the position of permanent elements such as walls and columns).

How to run the code?

For Building, add this flag to use only five threads -j 5; otherwise, the project might exit before building.

• After successfully building the project, all the input paths (to the query and central sessions, for example) and parameters are given in the file config/params.yaml.

• Then simply running the code (with the play bottom) should start the execution. in the console, the following should be visible:

  ----> Slam2ref starts.

• Once the program has been successfully finalized, you should see the following:

  ----> Slam2ref done.

License

For academic usage, the code is released under the GPLv3 license.

For any commercial purpose, please contact the author.

Citation:

If you use this work or our data in your research, please include the following citations (these BibTeX entries are the best versions you will likely find ✔️).

Paper & Data: The data consists of the BIM Model of ConSLAM and Ground Truth poses.

@article{SLAM2REF:vega2024:paper,
	title        = {{SLAM2REF}: advancing long-term mapping with {3D} {LiDAR} and reference map integration for precise 6-{DoF} trajectory estimation and map extension},
	author       = {Vega-Torres, Miguel A. and Braun, Alexander and Borrmann, André},
	year         = 2024,
	month        = 7,
	journal      = {Construction Robotics},
	publisher    = {Springer},
	volume       = 8,
	number       = 2,
	pages        = 13,
	doi          = {10.1007/s41693-024-00126-w},
	url          = {https://link.springer.com/article/10.1007/s41693-024-00126-w},
	notes        = {link to code: https://github.com/MigVega/SLAM2REF/. Link to data: https://mediatum.ub.tum.de/1743877},
	keywords     = {LiDAR; Multi-Session SLAM; Pose-Graph Optimization; Loop Closure; Long-term Mapping; Change Detection; {BIM} Update; {3D} Indoor Localization and Mapping},
	language     = {en}
}

@misc{SLAM2REF:vega2024:data,
	title        = {{ConSLAM} {BIM} and {GT} Poses},
	author       = {Vega-Torres, Miguel A. and Braun, Alexander and Borrmann, André},
	year         = 2024,
	month        = 6,
	publisher    = {Technical University of Munich},
	doi          = {10.14459/2024MP1743877},
	url          = {https://mediatum.ub.tum.de/1743877},
	type         = {Dataset},
	abstract     = {The ConSLAM BIM and GT Poses comprehends the 3D building information model (in IFC and Revit formats), manually elaborated based on the terrestrial laser scanner of the sequence 2 of ConSLAM, and the refined grounth truth (GT) poses (in TUM format) of the sessions 2, 3, 4 and 5 of the open-access Con{SLAM} dataset. This dataset can be found here: https://github.com/mac137/ConSLAM},
	keywords     = {LiDAR; Multi-Session SLAM; Pose-Graph Optimization; Loop Closure; Long-term Mapping; Change Detection; {BIM} Update; {3D} Indoor Localization and Mapping},
	language     = {en}
}

Code: To be added.

Acknowledgements

This is an extension of LT-SLAM(2022), whose author is Giseop Kim.