Upload semantic2d dataset

dataset/README.md

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-# Semantic2D: Enabling Semantic Scene Understanding with 2D Lidar Alone
-The Semantic2D Dataset and its raw rosbag data can be downloaded at: https://doi.org/10.5281/zenodo.13730200. 
+# Semantic2D Dataset
+
+A 2D lidar semantic segmentation dataset for mobile robotics applications. This is the first publicly available 2D lidar semantic segmentation dataset, featuring point-wise annotations for nine indoor object categories across twelve distinct environments.
+
+**Associated Paper:** *Semantic2D: Enabling Semantic Scene Understanding with 2D Lidar Alone*
+**Authors:** Zhanteng Xie, Yipeng Pan, Yinqiang Zhang, Jia Pan, Philip Dames
+**Institutions:** The University of Hong Kong, Temple University
+
+**Video:** https://youtu.be/P1Hsvj6WUSY
+**GitHub:** https://github.com/TempleRAIL/semantic2d
+
+---
+
+## Dataset Overview
+
+| Property | Value |
+|----------|-------|
+| Total Data Tuples | ~188,007 |
+| Recording Rate | 20 Hz |
+| Total Duration | ~131 minutes |
+| Environments | 12 indoor environments |
+| Buildings | 7 buildings (4 at Temple, 3 at HKU) |
+| LiDAR Sensors | 3 types |
+| Semantic Classes | 9 + background |
+
+### Semantic Classes
+
+| ID | Class | Description |
+|----|-------|-------------|
+| 0 | Background/Other | Unclassified objects |
+| 1 | Chair | Chairs and seating |
+| 2 | Door | Doors and doorways |
+| 3 | Elevator | Elevator doors |
+| 4 | Person | Dynamic pedestrians |
+| 5 | Pillar | Structural pillars |
+| 6 | Sofa | Sofas and couches |
+| 7 | Table | Tables |
+| 8 | Trash bin | Trash cans |
+| 9 | Wall | Walls and partitions |
+
+### LiDAR Sensors
+
+| Sensor | Robot | Location | Range (m) | FOV (deg) | Angular Res. (deg) | Points |
+|--------|-------|----------|-----------|-----------|-------------------|--------|
+| Hokuyo UTM-30LX-EW | Jackal | Temple | [0.1, 60] | 270 | 0.25 | 1,081 |
+| WLR-716 | Customized | HKU | [0.15, 25] | 270 | 0.33 | 811 |
+| RPLIDAR-S2 | Customized | HKU | [0.2, 30] | 360 | 0.18 | 1,972 |
+
+---
+
+## Directory Structure
+
+```
+semantic2d_dataset_2025/
+├── README.md
+│
+├── rosbags/                               # Original ROS bag files, only used for reference or to extract other data, such as RGB, depth, goal, etc.
+│   ├── 2024-04-04-12-16-41.bag            # Temple Engineering Lobby
+│   ├── 2024-04-04-13-48-45.bag            # Temple Engineering 6th floor
+│   ├── 2024-04-04-14-18-32.bag            # Temple Engineering 9th floor
+│   ├── 2024-04-04-14-50-01.bag            # Temple Engineering 8th floor
+│   ├── 2024-04-11-14-37-14.bag            # Temple Engineering 4th floor
+│   ├── 2024-04-11-15-24-29.bag            # Temple Engineering Corridor
+│   ├── 2025-07-08-13-32-08.bag            # Temple SERC Lobby
+│   ├── 2025-07-08-14-22-44.bag            # Temple Gladfelter Lobby
+│   ├── 2025-07-18-17-43-11.bag            # Temple Mazur Lobby
+│   ├── 2025-11-10-15-53-51.bag            # HKU Chow Yei Ching 4th floor
+│   ├── 2025-11-11-21-27-17.bag            # HKU Centennial Campus Lobby
+│   └── 2025-11-18-22-13-37.bag            # HKU Jockey Club 3rd floor
+│
+└── semantic2d_data/                        # Semantic2D Segmentation Data files
+    ├── dataset.txt                	    # dataset index for each folder
+    ├── 2024-04-04-12-16-41/                # Temple Engineering Lobby 		# Temple environments (Hokuyo)
+    ├── 2024-04-04-13-48-45/                # Temple Engineering 6th floor
+    ├── 2024-04-04-14-18-32/                # Temple Engineering 9th floor
+    ├── 2024-04-04-14-50-01/                # Temple Engineering 8th floor
+    ├── 2024-04-11-14-37-14/                # Temple Engineering 4th floor
+    ├── 2024-04-11-15-24-29/                # Temple Engineering Corridor
+    ├── 2025-07-08-13-32-08/                # Temple SERC Lobby
+    ├── 2025-07-08-14-22-44/		    # Temple Gladfelter Lobby
+    ├── 2025-07-18-17-43-11/                # Temple Mazur Lobby
+    ├── 2025-11-10-15-53-51/                # HKU Chow Yei Ching 4th floor	# HKU environments (WLR-716 + RPLIDAR)
+    ├── 2025-11-11-21-27-17/		    # HKU Centennial Campus Lobby
+    └── 2025-11-18-22-13-37/	            # HKU Jockey Club 3rd floo
+```
+
+---
+
+## Semantic2D Segmentation Data Folder Structure
+
+### Temple University Environments (Hokuyo UTM-30LX-EW)
+
+Folders: `2024-04-04-*` and `2024-04-11-*` (6 environments)
+
+```
+2024-04-04-12-16-41/                        # Engineering Lobby
+├── train.txt                               # Training split filenames (70%)
+├── dev.txt                                 # Validation split filenames (10%)
+├── test.txt                                # Test split filenames (20%)
+│
+├── scans_lidar/                            # LiDAR range data
+│   └── *.npy                               # Shape: (1081,) - range values in meters
+│
+├── intensities_lidar/                      # LiDAR intensity data
+│   └── *.npy                               # Shape: (1081,) - intensity values
+│
+├── line_segments/                          # Extracted line features
+│   └── *.npy                               # Line segments [x1,y1,x2,y2] per line
+│
+├── positions/                              # Robot poses
+│   └── *.npy                               # Shape: (3,) - [x, y, yaw] in map frame
+│
+├── velocities/                             # Velocity commands
+│   └── *.npy                               # Shape: (2,) - [linear_x, angular_z]
+│
+├── semantic_label/                         # Point-wise semantic labels
+│   └── *.npy                               # Shape: (1081,) - class ID per point
+│
+├── semantic_scan/                          # Semantic scan visualization data
+│   └── *.npy
+│
+├── final_goals_local/                      # Navigation final goals
+│   └── *.npy                               # Goal positions in local frame
+│
+├── sub_goals_local/                        # Navigation sub-goals
+│   └── *.npy                               # Sub-goal waypoints
+│
+└── 202404041210_eng_lobby_map/             # Environment map
+    ├── 202404041210_eng_lobby.pgm          # Occupancy grid map (PGM format)
+    ├── 202404041210_eng_lobby.yaml         # Map configuration
+    └── map_labelme/                        # Semantic labeled map
+        ├── img.png                         # Original map image
+        ├── label.png                       # Semantic label image
+        ├── label_viz.png                   # Colored visualization
+        ├── label_names.txt                 # Class name list
+        └── map_labelme.json                # LabelMe annotation file
+```
+
+### HKU Environments (WLR-716 + RPLIDAR-S2)
+
+Folders: `2025-11-*` (3 environments)
+
+```
+2025-11-10-15-53-51/                        # Chow Yei Ching 4th floor
+├── train.txt                               # Training split (70%)
+├── dev.txt                                 # Validation split (10%)
+├── test.txt                                # Test split (20%)
+│
+├── # WLR-716 LiDAR data (811 points, 270 deg FOV)
+├── scans_lidar_wlr716/                     # Range data
+│   └── *.npy                               # Shape: (811,)
+├── intensities_lidar_wlr716/               # Intensity data
+│   └── *.npy                               # Shape: (811,)
+├── line_segments_wlr716/                   # Line segments
+│   └── *.npy
+├── semantic_label_wlr716/                  # Semantic labels
+│   └── *.npy                               # Shape: (811,)
+│
+├── # RPLIDAR-S2 data (1972 points, 360 deg FOV)
+├── scans_lidar_rplidar/                    # Range data
+│   └── *.npy                               # Shape: (1972,)
+├── intensities_lidar_rplidar/              # Intensity data
+│   └── *.npy                               # Shape: (1972,)
+├── line_segments_rplidar/                  # Line segments
+│   └── *.npy
+├── semantic_label_rplidar/                 # Semantic labels
+│   └── *.npy                               # Shape: (1972,)
+│
+├── # Shared data (same for both sensors)
+├── positions/                              # Robot poses [x, y, yaw]
+│   └── *.npy                               # Shape: (3,)
+├── velocities/                             # Velocity commands [vx, wz]
+│   └── *.npy                               # Shape: (2,)
+│
+└── 202511101415_cyc_4th_map/               # Environment map
+    ├── *.pgm                               # Occupancy grid
+    ├── *.yaml                              # Map configuration
+    └── map_labelme/                        # Semantic labels
+        └── ...
+```
+
+---
+
+## Data Format Details
+
+### LiDAR Scan Data (`.npy`)
+
+```python
+import numpy as np
+
+# Load range data
+scan = np.load('scans_lidar/0000001.npy')  # Shape: (N,) where N = num_points
+
+# Hokuyo: N=1081, angle_min=-135°, angle_max=135°
+# WLR-716: N=811, angle_min=-135°, angle_max=135°
+# RPLIDAR-S2: N=1972, angle_min=-180°, angle_max=180°
+
+# Convert to Cartesian coordinates
+angles = np.linspace(angle_min, angle_max, num=N)
+x = scan * np.cos(angles)
+y = scan * np.sin(angles)
+```
+
+### Semantic Labels (`.npy`)
+
+```python
+# Load semantic labels
+labels = np.load('semantic_label/0000001.npy')  # Shape: (N,)
+
+# Each value is a class ID (0-9):
+# 0: Background, 1: Chair, 2: Door, 3: Elevator, 4: Person,
+# 5: Pillar, 6: Sofa, 7: Table, 8: Trash bin, 9: Wall
+```
+
+### Robot Pose (`.npy`)
+
+```python
+# Load robot pose
+pose = np.load('positions/0000001.npy')  # Shape: (3,)
+x, y, yaw = pose[0], pose[1], pose[2]  # Position and orientation in map frame
+```
+
+### Dataset Splits (`train.txt`, `dev.txt`, `test.txt`)
+
+```
+# Each line contains a .npy filename
+0001680.npy
+0007568.npy
+0009269.npy
+...
+```
+
+---
+
+## Semantic2D labeling:
+
+### 1. Data Collection (`dataset_collection.py`)
+
+ROS node for collecting data from the robot during teleoperation.
+
+**Key Features:**
+- Subscribes to LiDAR scans (`/scan, /wj716_base/scan`, `/rplidar_base/scan`)
+- Records range, intensity, line segments, positions, velocities
+- Saves data at 20 Hz as `.npy` files
+
+
+### 2. Semi-Automated Labeling Framework (SALSA)
+
+**Algorithm:**
+1. Load pre-labeled semantic map (from LabelMe)
+2. For each LiDAR scan:
+   - Extract line features for robust alignment
+   - Apply ICP to refine robot pose
+   - Project LiDAR points to map frame
+   - Match points to semantic labels via pixel lookup
+   - Points in free space labeled as "Person" (dynamic objects)
+
+**Configuration (modify in script):**
+```python
+DATASET_ODIR = "/path/to/raw/data"
+MAP_ORIGIN = np.array([-82.0, -71.6, 0.0])  # From map YAML
+MAP_RESOLUTION = 0.025
+POINTS = 811  # Number of LiDAR points
+AGNLE_MIN = -2.356  # Min angle (radians)
+AGNLE_MAX = 2.356   # Max angle (radians)
+```
+
+---
+
+## Environments Summary
+
+| Environment | Location | Folder | LiDAR |
+|-------------|----------|--------|-------|
+| Engineering Lobby | Temple | `2024-04-04-12-16-41` | Hokuyo |
+| Engineering 6th Floor | Temple | `2024-04-04-13-48-45` | Hokuyo |
+| Engineering 9th Floor | Temple | `2024-04-04-14-18-32` | Hokuyo |
+| Engineering 8th Floor | Temple | `2024-04-04-14-50-01` | Hokuyo |
+| Engineering 4th Floor | Temple | `2024-04-11-14-37-14` | Hokuyo |
+| Engineering Corridor | Temple | `2024-04-11-15-24-29` | Hokuyo |
+| SERC Lobby | Temple | `2025-07-08-13-32-08` | Hokuyo |
+| Gladfelter Lobby | Temple | `2025-07-08-14-22-44` | Hokuyo |
+| Mazur Lobby | Temple | `2025-07-18-17-43-11` | Hokuyo |
+| Chow Yei Ching 4th Floor | HKU | `2025-11-10-15-53-51` | WLR-716/RPLIDAR |
+| Centennial Campus Lobby | HKU | `2025-11-11-21-27-17` | WLR-716/RPLIDAR |
+| Jockey Club 3rd Floor | HKU | `2025-11-18-22-13-37` | WLR-716/RPLIDAR |
+
+---
+
+## ROS Bag Contents
+
+The original ROS bags in `rosbags/` contain:
+
+| Topic | Message Type | Description |
+|-------|--------------|-------------|
+| `/scan` or `/*/scan` | `sensor_msgs/LaserScan` | LiDAR scans |
+| `/robot_pose` | `geometry_msgs/PoseStamped` | Robot pose |
+| `/cmd_vel` | `geometry_msgs/Twist` | Velocity commands |
+| `/tf` | `tf2_msgs/TFMessage` | Transforms |
+| `/map` | `nav_msgs/OccupancyGrid` | Occupancy map |
+| `/camera/*` | `sensor_msgs/Image` | RGB/Depth images |
+| `/odom` | `nav_msgs/Odometry` | Odometry |
+
+---
+
+## Related Resources
+
+- **SALSA (Dataset and Labeling Framework):** https://github.com/TempleRAIL/semantic2d
+- **S3-Net (Segmentation Algorithm):** https://github.com/TempleRAIL/s3_net
+- **Semantic CNN Navigation:** https://github.com/TempleRAIL/semantic_cnn_nav
+- **Dataset Zenodo:** DOI: 10.5281/zenodo.18350696
+
+---
+
+## Citation
+
+```bibtex
+@article{xie2026semantic2d,
+  title={Semantic2D: Enabling Semantic Scene Understanding with 2D Lidar Alone},
+  author={Xie, Zhanteng and Pan, Yipeng and Zhang, Yinqiang and Pan, Jia and Dames, Philip},
+  journal={arXiv preprint arXiv:2409.09899},
+  year={2026}
+}
+```
+
+---
+
+## License
+
+Please refer to the associated paper and GitHub repository for licensing information.
+
+## Contact
+
+- Zhanteng Xie: zhanteng@hku.hk
+

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