Implementation Details of Comparative Experiments
This project primarily involves learning and testing open-source algorithms related to LiDAR SLAM (Simultaneous Localization and Mapping), as well as adapting them to different models of LiDAR sensors. All components of the project can be compiled and run in environments with Ubuntu 20.04, ROS1, and OpenCV 4.2.
For detailed information on related projects, please refer to the official homepage.
Below is a table indicating the testing progress of each project, showing which ones have been tested and which ones are pending:
| Project | Tested | Notes |
|---|---|---|
| DLIO | ❌ No | Ongoing |
| FAST-LIO2 | ✅ Yes | |
| Faster-LIO | ✅ Yes | |
| IG-LIO | ✅ Yes | |
| LIO-SAM | ✅ Yes | |
| LIO-Lite | ✅ Yes | |
| LOG-LIO | ✅ Yes | |
| MA-LIO | ❌ No | Ongoing |
| PV-LIO | ✅ Yes | |
| SLICT | ❌ No | Ongoing |
| VoxelMap | ✅ Yes |
Comprehensive testing has not yet been completed, and we are expanding our test data to include more diverse datasets. We are working diligently to finalize all tests and will provide detailed reports—including our testing conditions, data, and results—in the near future.
I would like to extend my deepest gratitude to the contributors of the aforementioned open-source projects for their invaluable contributions to the open-source community. Their dedication has significantly advanced the field, and I have learned a great deal from their exemplary work. If this project inadvertently infringes upon any rights or contains any errors, please feel free to contact me at lswang@mail.ecust.edu.cn. Your feedback is highly appreciated and will help improve the project. Once again, thank you sincerely for your contributions and support.