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+# <div align = "center"><img src="assets/logo.png" width="5%" height="5%" /> SLABIM: </div>
+
+## <div align = "center">A SLAM-BIM Coupled Dataset in HKUST Main Building</div>
+
+<div align="center">
+<a href="https://arxiv.org/abs/2502.16856"><img src="https://img.shields.io/badge/Paper-IEEE ICRA-004088.svg"/></a>
+<!-- <a href="https://ieeexplore.ieee.org/document/10518010"><img src="https://img.shields.io/badge/Paper-ICRA-blue"/></a>
+<a href="https://arxiv.org/abs/2308.11573"><img src="https://img.shields.io/badge/ArXiv-2308.11573-004088.svg"/></a> -->
+<a href="https://www.youtube.com/watch?v=7NckgY15ABQ">
+<img alt="Youtube" src="https://img.shields.io/badge/Video-Youtube-red"/>
+</a>
+<a ><img alt="PRs-Welcome" src="https://img.shields.io/badge/PRs-Welcome-red" /></a>
+<a href="https://github.com/HKUST-Aerial-Robotics/SLABIM/issues">
+<img alt="Issues" src="https://img.shields.io/github/issues/HKUST-Aerial-Robotics/SLABIM?color=0088ff"/>
+</a>
+</div>
+
+> Haoming Huang, [Zhijian Qiao](https://qiaozhijian.github.io/), Zehuan Yu, Chuhao Liu, [Shaojie Shen](https://uav.hkust.edu.hk/group/), Fumin Zhang and [Huan Yin](https://huanyin94.github.io/)
+>
+> Submitted to 2025 IEEE International Conference on Robotics & Automation
+
+### News
+* **`17 Feb 2025`:** Download Links Updated.
+* **`28 Jan 2025`:** Accepted by ICRA 2025.
+* **`15 Sep 2024`:** We submit our paper to [IEEE ICRA](https://2025.ieee-icra.org/).
+
+## Download
+**Please click these below links to download:**
+
++ [**Calibration Files**](https://hkustconnect-my.sharepoint.com/:f:/g/personal/hhuangce_connect_ust_hk/EsRF4KSE2QNJhNe5pkGnlhsBjF2A4Y0_t6DhoPypFN3TnA)
++ [**BIM**](https://hkustconnect-my.sharepoint.com/:f:/g/personal/hhuangce_connect_ust_hk/EsFggIKoN01Hk6ZIKSrCLa4BuvIo4ut4I_Da9WmEgvxMqQ)
++ [**Sensor Data**](https://hkustconnect-my.sharepoint.com/:f:/g/personal/hhuangce_connect_ust_hk/Eu9IRQfbPJpGnTtmjgkrrigBopCUTe2gBJDAp8m5vqZZRw)
+## Abstract
+<div align="center"><h4>SLABIM is an open-sourced SLAM dataset that couples with BIM (Building Information Modeling).</h4></div>
+
+<div align = "center"><img src="assets/overview.png" width="95%" /> </div>
+
+**Features**:
++ **Large-scale Building Information Modeling**: The BIM model of this dataset is a part of the digital twin project in HKUST, 
+featuring various types of offices, classrooms, lounges, and corridors.
++ **Multi-session & Multi-sensor Data**: We collect 12 sessions across different floors and regions. These sessions encompass various indoor scenarios.
++ **Dataset Validation**: To demonstrate the practicality of SLABIM, we test three different tasks: (1) LiDAR-to-BIM registration, and (2) Robot pose tracking on BIM and (3) Semantic mapping. 
+
+## Dataset Structure
+1. ```BIM/``` contains CAD files (.dxf) and mesh files (.ply) exported from the original BIM models, organized by storey and semantic tags. Users can sample
+the meshes at specific densities to obtain point clouds, offering flexibility for various robotic tasks.
+
+2. ```calibration files``` provide intrinsic camera parameters and the extrinsic parameters to the LiDAR.
+
+3. In ```sensor data/``` directory, each session is named
+```<X>F Region<Y>```, with X=1,3,4,5 and Y=1,2,3
+indicating the storey and region of collection, such
+as ```3F Region1```. This directory contains the **images**
+and **points** produced by **camera** and **LiDAR**.
+
+4. ```data <x>.bag```, x=0,1,2... is the **rosbag** encoding the raw information, which can be parsed using ROS tools.
+
+5. ```sensor data/``` also contains the maps generated by SLAM, including **submap** for the LiDAR-to-BIM registration and **optimized map** by the offline mapping system.
+
+6. ```pose_frame_to_bim.txt```, ```pose_map_to_bim.txt``` and ```pose_submap_to_bim.txt``` contains the **ground truth poses** from LiDAR scans and maps to the BIM coordinate. These poses are finely tuned using a manually
+provided initial guess and local point cloud alignment.
+
+```
+SLABIM
+├── BIM
+│   └── <X>F
+│       ├── CAD
+│       │   └── <X>F.dxf
+│       └── mesh
+│           ├── columns.ply
+│           ├── doors.ply
+│           ├── floors.ply
+│           └── walls.ply
+├── calibration_files
+│   ├── cam_intrinsics.txt
+│   └── cam_to_lidar.txt
+└── sensor_data
+    └── <X>F_Region<Y>
+        ├── images
+        │   ├── data
+        │   │   └── <frame_id>.png
+        │   └── timestamps.txt
+        ├── map
+        │   ├── data
+        │   │   ├── colorized.las
+        │   │   └── uncolorized.ply
+        │   └── pose_map_to_bim.txt
+        ├── points
+        │   ├── data
+        │   │   └── <frame_id>.pcd
+        │   ├── pose_frame_to_bim.txt
+        │   └── timestamps.txt
+        ├── rosbag
+        │   └── data_<x>.bag
+        └── submap
+            ��── data
+            │   └── <submap_id>.pcd
+            └── pose_submap_to_bim.txt
+```
+
+
+<!-- ## Multi-session SLAM Dataset
+<div align="left">
+<img src="assets/1F.png" width=28.6% />
+<img src="assets/3Fto5F.png" width=30.6% />
+<img src="assets/colormap.gif" width = 39.3% >
+</div> -->
+
+## Data Acquisition Platform
+The handheld sensor suite is illustrated in the Figure 1. A more detailed summary of the characteristics can be found in the Table 1.
+<div align="left">
+<img src="assets/sensor_suite.png" width=38.3% />
+<img src="assets/collection.gif" width = 60.6% >
+</div>
+
+## Qualitative Results on SLABIM
+### Global LiDAR-to-BIM Registration
+Global LiDAR-to-BIM registration aims to estimate a transformation from scratch between the LiDAR submap and the BIM coordinate system. A robot can localize itself globally by aligning the online built submap to the BIM. 
+
+<div align = "center"><img src="assets/registration.gif" width="35%" /> </div>
+
+### Robot Pose Tracking on BIM
+Different from LiDAR-to-BIM, Pose tracking requires estimating poses given the initial state and sequential measurements. 
+
+<div align = "center"><img src="assets/pose_tracking.gif" width="35%" /> </div>
+
+### Semantic Mapping
+We deploy [FM-Fusion](https://arxiv.org/abs/2402.04555)[1] on SLABIM. For the ground truth, we convert the HKUST BIM into semantic point cloud maps using the semantic tags in BIM. Both maps contain four semantic categories: floor, wall, door, and
+column, the common elements in indoor environments
+<div align = "center"><img src="assets/semantic_mapping.gif" width="35%" /> </div>
+
+[1] C. Liu, K. Wang, J. Shi, Z. Qiao, and S. Shen, “Fm-fusion: Instance-
+aware semantic mapping boosted by vision-language foundation mod-
+els,” IEEE Robotics and Automation Letters, 2024
+## Acknowledgements
+We sincerely thank Prof. Jack C. P. Cheng for generously
+providing the original HKUST BIM files.
+
+<!-- ## Citation
+If you find SLABIM is useful in your research or applications, please consider giving us a star 🌟 and citing it by the following BibTeX entry. -->
+<!-- ```bibtex
+@ARTICLE{qiao2024g3reg,
+  author={Qiao, Zhijian and Yu, Zehuan and Jiang, Binqian and Yin, Huan and Shen, Shaojie},
+  journal={IEEE Transactions on Automation Science and Engineering}, 
+  title={G3Reg: Pyramid Graph-Based Global Registration Using Gaussian Ellipsoid Model}, 
+  year={2024},
+  volume={},
+  number={},
+  pages={1-17},
+  keywords={Point cloud compression;Three-dimensional displays;Laser radar;Ellipsoids;Robustness;Upper bound;Uncertainty;Global registration;point cloud;LiDAR;graph theory;robust estimation},
+  doi={10.1109/TASE.2024.3394519}}
+```
+```bibtex
+@inproceedings{qiao2023pyramid,
+  title={Pyramid Semantic Graph-based Global Point Cloud Registration with Low Overlap},
+  author={Qiao, Zhijian and Yu, Zehuan and Yin, Huan and Shen, Shaojie},
+  booktitle={2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
+  pages={11202--11209},
+  year={2023},
+  organization={IEEE}
+}
+``` -->