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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`)

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`)

# 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`)

# 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:

Load pre-labeled semantic map (from LabelMe)
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):

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

@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