Semantic2D: Enabling Semantic Scene Understanding with 2D Lidar Alone

Implementation code for our paper "Semantic2D: Enabling Semantic Scene Understanding with 2D Lidar Alone". Video demos can be found at multimedia demonstrations. The Semantic2D dataset can be found and downloaded at: https://doi.org/10.5281/zenodo.18350696.

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Related Resources

• Dataset Download: https://doi.org/10.5281/zenodo.18350696
• SALSA (Dataset and Labeling Framework): https://github.com/TempleRAIL/semantic2d
• S³-Net (Stochastic Semantic Segmentation): https://github.com/TempleRAIL/s3_net
• Semantic CNN Navigation: https://github.com/TempleRAIL/semantic_cnn_nav

Demos

S³-Net Segmentation

Semantic Mapping

Semantic Navigation

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Requirements

• Ubuntu 20.04
• ROS Noetic
• Python 3.8
• Labelme
• scikit-learn
• tqdm
• PyTorch
• NumPy
• Pillow

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Installation

# Clone the repository
git clone https://github.com/TempleRAIL/semantic2d.git
cd semantic2d

# Install Python dependencies
pip install labelme scikit-learn tqdm torch numpy pillow

# Install LabelMe configuration with pre-defined semantic classes
cp salsa/manually_labeling/.labelmerc ~/.labelmerc

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Semantic2D Dataset Description

The dataset contains the following data types:

Folder	Description	Shape
scans_lidar/	2D LiDAR range data	(N,) array
intensities_lidar/	2D LiDAR intensity data	(N,) array
line_segments/	Extracted line segments	List of [x1,y1,x2,y2]
positions/	Robot position in map frame	(3,) array [x, y, yaw]
velocities/	Robot velocity commands	(2,) array [Vx, Wz]
semantic_label/	Point-wise semantic labels	(N,) array

Semantic Classes

ID	Class	Color (RGB)
0	Other/Background	-
1	Chair	(109, 0, 156)
2	Door	(0, 46, 221)
3	Elevator	(0, 164, 187)
4	Person	(204, 204, 204)
5	Pillar	(0, 155, 18)
6	Sofa	(0, 225, 0)
7	Table	(203, 249, 0)
8	Trash bin	(255, 173, 0)
9	Wall	(227, 0, 0)

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Semi-Automatic Labeling Usage

SALSA (Semi-Automatic Labeling framework for Semantic Annotation) consists of three steps:

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Step 1: Data Collection

Collect and save data from a rosbag file. Prerequisites: You should have already created an environment map using a mapping package (e.g., gmapping) and collected raw rosbag data.

1.1 Configure Data Collection

Edit salsa/manually_labeling/dataset_collection.py:

################ CUSTOMIZATION REQUIRED ################

# Number of LiDAR points (must match your sensor)
POINTS = 1081  # Hokuyo: 1081, WLR-716: 811, RPLIDAR-S2: 1972

# Output directory for collected data
DATA_PATH = "~/semantic2d_data/2024-04-11-15-24-29"

1.2 Configure Line Extraction Launch File

The laser_line_extraction package extracts line features from LiDAR scans for ICP alignment. You must configure it for your LiDAR sensor.

Edit salsa/manually_labeling/semantic_data_collection_ws/src/laser_line_extraction/launch/example.launch:

<launch>
  <node name="line_extractor" pkg="laser_line_extraction" type="line_extraction_node">
    <!--################ CUSTOMIZATION REQUIRED ################-->

    <!-- LiDAR frame ID (from your URDF or tf tree) -->
    <param name="~frame_id" value="rear_laser" />

    <!-- LiDAR scan topic name -->
    <param name="~scan_topic" value="scan" />

    <!-- Sensor range parameters (must match your LiDAR) -->
    <param name="~min_range" value="0.6" />
    <param name="~max_range" value="60.0" />

    <!--################ Usually no changes needed below ################-->
    <param name="~frequency" value="30.0" />
    <param name="~publish_markers" value="false" />
    <param name="~bearing_std_dev" value="1e-5" />
    <param name="~range_std_dev" value="0.02" />
    <param name="~least_sq_angle_thresh" value="0.0001" />
    <param name="~least_sq_radius_thresh" value="0.0001" />
    <param name="~max_line_gap" value="1.0" />
    <param name="~min_line_length" value="0.4" />
    <param name="~min_split_dist" value="0.04" />
    <param name="~outlier_dist" value="0.06" />
    <param name="~min_line_points" value="15" />
  </node>
</launch>

Key parameters to change:

Parameter	Description	Example Values
frame_id	TF frame of your LiDAR	laser, base_scan, rear_laser, rplidar_link
scan_topic	ROS topic for LiDAR scans	scan, /scan, /rplidar/scan
min_range	Minimum valid range (m)	Hokuyo: 0.1, WLR-716: 0.15, RPLIDAR: 0.2
max_range	Maximum valid range (m)	Hokuyo: 60.0, WLR-716: 25.0, RPLIDAR: 30.0

How to find your frame_id:

# Method 1: From rostopic
rostopic echo /scan --noarr -n 1 | grep frame_id

# Method 2: From tf tree
rosrun tf view_frames  # Creates frames.pdf

1.3 Configure ROS Topics for Data Collection

The default ROS topic subscriptions in dataset_collection.py are:

Topic	Message Type	Description
scan	sensor_msgs/LaserScan	LiDAR scan data
line_segments	laser_line_extraction/LineSegmentList	Line features
bluetooth_teleop/cmd_vel	geometry_msgs/Twist	Velocity commands
robot_pose	geometry_msgs/PoseStamped	Robot pose

To customize for your robot, modify the subscribers in dataset_collection.py:

# Example: For Hokuyo UTM-30LX-EW lidar
self.scan_sub = rospy.Subscriber("/scan", LaserScan, self.scan_callback)
self.dwa_cmd_sub = rospy.Subscriber('/cmd_vel', Twist, self.dwa_cmd_callback)
self.robot_pose_pub = rospy.Subscriber('/amcl_pose', PoseWithCovarianceStamped, self.robot_pose_callback)

# Example: For custom robot with namespaced topics
self.scan_sub = rospy.Subscriber("/my_robot/laser/scan", LaserScan, self.scan_callback)

1.4 Run Data Collection

# Terminal 1: Start ROS master
roscore

# Terminal 2: Compile and launch line extraction
cd salsa/manually_labeling/semantic_data_collection_ws
catkin_make
source devel/setup.bash
roslaunch laser_line_extraction example.launch

# Terminal 3: Start data collection
cd salsa/manually_labeling
python dataset_collection.py

# Terminal 4: Play rosbag
rosbag play your_data.bag

1.5 Generate Train/Dev/Test Splits

After data collection, generate index files (train.txt, dev.txt, test.txt) that define the dataset splits.

Configure salsa/manually_labeling/generateTrainDevSet.py:

################ CUSTOMIZATION REQUIRED ################
# The path of your dataset folder:
train_folder = '~/semantic2d_data/2024-04-11-15-24-29'

# Split percentages (must sum to 1.0)
TRAIN_RATIO = 0.70  # 70% for training
DEV_RATIO = 0.10    # 10% for validation/development
TEST_RATIO = 0.20   # 20% for testing
########################################################

Run the script:

cd salsa/manually_labeling
python generateTrainDevSet.py

Example output:

Dataset folder: /home/user/semantic2d_data/2024-04-11-15-24-29
Total samples: 20427
Split ratios: Train=70%, Dev=10%, Test=20%
Split sizes:  Train=14298, Dev=2042, Test=4087

Generated split files:
  - /home/user/.../train.txt
  - /home/user/.../dev.txt
  - /home/user/.../test.txt
Done!

The script automatically:

• Counts total samples in the positions/ folder
• Calculates split sizes based on the defined ratios
• Shuffles data randomly before splitting
• Generates the three .txt files with sample filenames

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Step 2: Manual Labeling

Use LabelMe to manually label the environment map with semantic classes.

2.1 Run LabelMe

labelme

# Optionally, use RViz to visualize RGB images while labeling:
roscore
rosbag play your_data.bag
rviz  # Add Image display for camera topic

2.2 Labeling Process

1. Open your occupancy grid map image (.pgm or .png)
2. Create polygons around each object (Ctrl+N)
3. Select class from dropdown: Chair, Door, Elevator, Pillar, Sofa, Table, Trash bin, Wall
4. DO NOT label people - they are automatically detected as dynamic objects
5. Save as .json file

Demo: How to use LabelMe

Keyboard shortcuts:

Shortcut	Action
Ctrl+N	Create new polygon
Ctrl+S	Save annotation
Ctrl+Z	Undo
Delete	Delete selected polygon
Ctrl+E	Edit label
D	Next image
A	Previous image

2.3 Export Labeled Map

# Export to label images
labelme_export_json your_map.json -o labelme_output

# For older labelme versions:
# labelme_json_to_dataset your_map.json -o labelme_output

Output structure (see labelme_output):

labelme_output/
├── img.png           # Original map image
├── label.png         # Semantic label image (class IDs)
├── label_viz.png     # Colored visualization
└── label_names.txt   # Class name list

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Step 3: Automatic Labeling

Use ICP-based scan matching to automatically transfer labels from the map to each LiDAR scan.

3.1 Configure Automatic Labeling

Edit salsa/automatic_labeling/semi_automated_labeling_framework.py:

################ CUSTOMIZATION REQUIRED ################

# Dataset paths
DATASET_ODIR = "/home/user/semantic2d_data/2024-04-04-12-16-41"
DATASET_NAME = "train"  # Options: train, dev, test

# Map parameters (from your_map.yaml file)
MAP_ORIGIN = np.array([-21.200000, -34.800000, 0.000000])  # [x, y, theta]
MAP_RESOLUTION = 0.025000  # meters per pixel

# Labeled map paths (from Step 2)
MAP_LABEL_PATH = '../manually_labeling/labelme_output/label.png'
MAP_PATH = '../manually_labeling/labelme_output/img.png'

# LiDAR sensor parameters (see Customization section below)
POINTS = 1081
AGNLE_MIN = -2.356194496154785  # -135 degrees in radians
AGNLE_MAX = 2.356194496154785   # +135 degrees in radians
RANGE_MAX = 60.0

# URDF: LiDAR to base_link transformation
JOINT_XYZ = [-0.12, 0.0, 0.0]   # [x, y, z] translation
JOINT_RPY = [0.0, 0.0, 0.0]     # [roll, pitch, yaw] rotation

3.2 Run Automatic Labeling

cd salsa/automatic_labeling
python semi_automated_labeling_framework.py

What the algorithm does:

1. For each LiDAR scan:
   • Extract line features (stable structures like walls)
   • Use ICP to refine robot pose alignment with the map
   • Project LiDAR points to map coordinates
   • Match each point to semantic labels via pixel lookup
   • Points in free space → labeled as "Person" (dynamic objects)

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Customizing for Different LiDAR Sensors

Supported Sensor Configurations

The code includes pre-configured parameters for three sensors:

Parameter	Hokuyo UTM-30LX-EW	WLR-716	RPLIDAR-S2
POINTS	1081	811	1972
ANGLE_MIN (rad)	-2.356 (-135°)	-2.356 (-135°)	-3.142 (-180°)
ANGLE_MAX (rad)	2.356 (+135°)	2.356 (+135°)	3.142 (+180°)
RANGE_MIN (m)	0.1	0.15	0.2
RANGE_MAX (m)	60.0	25.0	30.0
JOINT_XYZ	[-0.12, 0, 0]	[0.065, 0, 0.182]	[0.065, 0, 0.11]
JOINT_RPY	[0, 0, 0]	[3.14, 0, 0]	[0, 0, 3.14]
frame_id (launch)	rear_laser	wlr716_link	rplidar_link
scan_topic (launch)	scan	/wj716_base/scan	/rplidar_base/scan

How to Configure Your Own Sensor

Method 1: From ROS Topic

# Get sensor parameters from ROS
rostopic echo /scan --noarr -n 1

# Output shows:
# angle_min: -2.356...
# angle_max: 2.356...
# angle_increment: 0.00436...
# range_max: 60.0
# ranges: <array with N elements>

Method 2: Calculate from Specifications

import numpy as np

# From your sensor datasheet
fov_degrees = 270           # Field of view
angular_resolution = 0.25   # Degrees per point

# Calculate parameters
points = int(fov_degrees / angular_resolution) + 1  # = 1081
angle_min = -np.radians(fov_degrees / 2)  # = -2.356
angle_max = np.radians(fov_degrees / 2)   # = +2.356

URDF Transformation (JOINT_XYZ, JOINT_RPY)

Find the LiDAR mounting position from your robot's URDF file:

<!-- In your robot.urdf -->
<joint name="laser_joint" type="fixed">
  <origin xyz="-0.12 0.0 0.0" rpy="0 0 0"/>
  <parent link="base_link"/>
  <child link="laser_frame"/>
</joint>

# Use these values in the config
JOINT_XYZ = [-0.12, 0.0, 0.0]  # From xyz attribute
JOINT_RPY = [0.0, 0.0, 0.0]    # From rpy attribute

Complete Example: Adding a New Sensor

In semi_automated_labeling_framework.py:

################ CUSTOMIZATION REQUIRED ################

# Comment out existing configuration
# # Hokuyo UTM-30LX-EW:
# POINTS = 1081
# AGNLE_MIN = -2.356194496154785
# ...

# Add YOUR sensor configuration:
# SICK TiM561:
POINTS = 811              # From rostopic echo /scan
AGNLE_MIN = -2.356        # -135 degrees
AGNLE_MAX = 2.356         # +135 degrees
RANGE_MAX = 10.0          # 10 meters

# URDF transformation (from robot model)
JOINT_XYZ = [0.15, 0.0, 0.2]   # Mounted 15cm forward, 20cm up
JOINT_RPY = [0.0, 0.0, 0.0]    # No rotation

Modifying Data Collection for Your Robot

In dataset_collection.py:

################ CUSTOMIZATION REQUIRED ################

# 1. Set number of points for your sensor
POINTS = 811  # Your sensor's point count

# 2. Set output directory
DATA_PATH = "~/my_robot_data/environment_1"

# 3. Modify ROS subscribers for your topics (in __init__):

# Original (Jackal robot):
self.scan_sub = rospy.Subscriber("scan", LaserScan, self.scan_callback)
self.dwa_cmd_sub = rospy.Subscriber('bluetooth_teleop/cmd_vel', Twist, self.dwa_cmd_callback)
self.robot_pose_pub = rospy.Subscriber('robot_pose', PoseStamped, self.robot_pose_callback)

# For YOUR robot (example):
self.scan_sub = rospy.Subscriber("/my_robot/scan", LaserScan, self.scan_callback)
self.dwa_cmd_sub = rospy.Subscriber('/my_robot/cmd_vel', Twist, self.dwa_cmd_callback)
self.robot_pose_pub = rospy.Subscriber('/amcl_pose', PoseWithCovarianceStamped, self.robot_pose_callback)

Quick Reference: Files to Modify

Task	File	Parameters to Change
Line Extraction	salsa/manually_labeling/semantic_data_collection_ws/src/laser_line_extraction/launch/example.launch	frame_id, scan_topic, min_range, max_range
Data Collection	salsa/manually_labeling/dataset_collection.py	POINTS, DATA_PATH, ROS topics
Dataset Splits	salsa/manually_labeling/generateTrainDevSet.py	train_folder, TRAIN_RATIO, DEV_RATIO, TEST_RATIO
Manual Labeling	~/.labelmerc	Label names/colors (optional)
Auto Labeling	salsa/automatic_labeling/semi_automated_labeling_framework.py	DATASET_ODIR, MAP_*, POINTS, ANGLE_*, JOINT_*
Visualization	salsa/automatic_labeling/draw_semantic_label_sample.py	DATASET_ODIR, POINTS, ANGLE_*

Launch File Configurations for Different Sensors

Hokuyo UTM-30LX-EW (Jackal robot):

<param name="~frame_id" value="rear_laser" />
<param name="~scan_topic" value="scan" />
<param name="~min_range" value="0.1" />
<param name="~max_range" value="60.0" />

WLR-716 (Custom robot):

<param name="~frame_id" value="wlr716_link" />
<param name="~scan_topic" value="/wj716_base/scan" />
<param name="~min_range" value="0.15" />
<param name="~max_range" value="25.0" />

RPLIDAR-S2 (Custom robot):

<param name="~frame_id" value="rplidar_link" />
<param name="~scan_topic" value="/rplidar_base/scan" />
<param name="~min_range" value="0.2" />
<param name="~max_range" value="30.0" />

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Visualization

Plot the labeled semantic LiDAR data:

# Configure the same sensor parameters in draw_semantic_label_sample.py
cd salsa/automatic_labeling
python draw_semantic_label_sample.py

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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}
}