PCD to LaserScan

https://github.com/ros-perception/pointcloud_to_laserscan/tree/humble

this package helps in converting the live PCl data coming from the 3D Li-DaR to a LaserScan format. Basically it converts the the yopic format.

It compresses the data from 3D to 2d using 2 different methods:

crops the data.
projects the leftover data to XY plane

Key Idea

Many robots use 2D LiDAR-based algorithms (like SLAM and localization).
However, modern sensors often produce 3D point clouds.

This package allows robots to:

Use 3D LiDAR / depth cameras
But still run 2D laser-based navigation algorithms

1. Main Functional Components

The package contains two ROS2 nodes:

PointCloudToLaserScanNode
LaserScanToPointCloudNode

Each node converts data between two ROS message types.

2. PointCloudToLaserScanNode

Purpose

This node converts:

sensor_msgs/PointCloud2 → sensor_msgs/LaserScan It projects a 3D point cloud onto a 2D plane to simulate a laser scan.

Example Use Case

A robot has a 3D LiDAR, but the navigation stack requires a 2D laser scan.

Workflow:

3D LiDAR
↓
PointCloud2
↓
pointcloud_to_laserscan node
↓
LaserScan
↓
SLAM / Navigation

This allows compatibility with:

AMCL localization
2D SLAM
Navigation2 stack

Subscribed Topics

The node subscribes to: cloud_in (sensor_msgs/msg/PointCloud2)

This topic contains the 3D point cloud input.

Important behavior:

The node processes data only when someone subscribes to the output scan topic.

This avoids unnecessary computation.

Published Topics

The node publishes: scan (sensor_msgs/msg/LaserScan)

3. Conversion Algorithm (Conceptual)

The conversion follows these steps:

Step 1 — Receive Point Cloud

The node receives a 3D point cloud containing many points:

(x, y, z)

Step 2 — Height Filtering

Points outside a specific vertical range are removed.

Parameters:

min_height
max_height

This isolates a horizontal slice of the environment.

Step 3 — Angle Calculation

Each point is converted into polar coordinates:

angle = atan2(y, x)
distance = sqrt(x² + y²)

Step 4 — Angular Binning

The scan space is divided into angular bins.

Example:

angle_min = -π
angle_max = π
angle_increment = 1°

Each bin represents one laser beam.

Step 5 — Closest Point Selection

For each angle bin:

The closest point is chosen.
That distance becomes the laser range.

This mimics how a real LiDAR works.

Step 6 — Publish LaserScan

The node generates a LaserScan message containing:

angle range
range measurements
scan timing
frame information

4. Key Parameters of PointCloudToLaserScanNode

These parameters control the conversion behavior.

1. Height Filtering

`min_height`

Minimum height of points to consider.

Removes points below ground.

`max_height`

Maximum height of accepted points.

Removes ceiling points.

2. Angular Limits

`angle_min`

Minimum scan angle.

`angle_max`

Maximum scan angle.

`angle_increment`

Resolution of the scan.

3. Range Limits

`range_min`

Minimum measurable distance.

`range_max`

Maximum measurable distance.

4. Frame Transformation

`target_frame`

Transforms the point cloud into another coordinate frame before conversion.

`transform_tolerance`

Allowed delay when looking up transforms.

5. Output Behavior

`use_inf`

Controls how empty ranges are represented.

If enabled:

range = +∞

Otherwise:

range = range_max + 1

6. Queue Size

`queue_size`

Controls how many messages are buffered.

Default:

number of CPU cores

7. Scan Time

`scan_time`

Defines how long a full scan takes. Used only to populate the LaserScan message.

5. Repository Structure

The repository contains typical ROS2 package files.

pointcloud_to_laserscan/
│
├── include/
│ └── pointcloud_to_laserscan
│
├── src/
│ ├── pointcloud_to_laserscan_node.cpp
│ ├── laserscan_to_pointcloud_node.cpp
│
├── launch/
│ └── launch files
│
├── CMakeLists.txt
├── package.xml
├── README.md
└── LICENSE

Many navigation stacks expect LaserScan.

Example:

3D LiDAR
↓
PointCloud2
↓
pointcloud_to_laserscan
↓
LaserScan
↓
Nav2

7. Limitations

Information Loss

Converting 3D → 2D removes vertical information.

Example:

Overhang obstacles
Multi-level objects

Assumes Flat Environment

Works best for:

indoor robots
ground robots

Not ideal for aerial robots.