ros_pointcloud2

ROS PointCloud2

A PointCloud2 message conversion library.

ros_pointcloud2 uses its own type for the message PointCloud2Msg to keep the library framework agnostic. ROS1 and ROS2 are supported with feature flags.

Get started with the example below, check out the other use cases in the examples folder or see the Documentation for a complete guide.

Quickstart

use ros_pointcloud2::prelude::*;

// PointXYZ (and many others) are provided by the crate.
let cloud_points = vec![
  PointXYZI::new(91.486, -4.1, 42.0001, 0.1),
  PointXYZI::new(f32::MAX, f32::MIN, f32::MAX, f32::MIN),
];

let out_msg = PointCloud2Msg::try_from_vec(cloud_points).unwrap();

// Convert the ROS crate message type, we will use r2r here.
// let msg: r2r::sensor_msgs::msg::PointCloud2 = out_msg.into();
// Publish ...

// ... now incoming from a topic.
// let in_msg: PointCloud2Msg = msg.into();
let in_msg = out_msg;

let processed_cloud = in_msg.try_into_iter().unwrap()
  .map(|point: PointXYZ| { // Define the info you want to have from the Msg.
      // Some logic here ...

      point
  })
  .collect::<Vec<_>>();

Integrations

There are currently 3 integrations for common ROS crates.

• rosrust_msg
  • 
• r2r_msg
  • 
  • 
  • 
  • 
• rclrs_msg
  • 
  • 
  • 

You can use rosrust and r2r by enabling the respective feature:

[dependencies]
ros_pointcloud2 = { version = "*", features = ["r2r_msg"]}
# or
ros_pointcloud2 = { version = "*", features = ["rosrust_msg"]}

rclrs (ros2_rust)

Features do not work properly with rcrls because the messages are linked externally. You need to use tags instead:

[dependencies]
ros_pointcloud2 = { git = "https://github.com/stelzo/ros_pointcloud2", tag = "v0.5.1_rclrs" }

Also, indicate the following dependencies to your linker inside the package.xml of your package.

<depend>std_msgs</depend>
<depend>sensor_msgs</depend>
<depend>builtin_interfaces</depend>

Please open an issue or PR if you need other integrations.

Performance

This library offers a speed up when compared to PointCloudLibrary (PCL) conversions but the specific factor depends heavily on the use case and system. See this repository for a detailed benchmark.

For minimizing the conversion overhead in general, always use the functions that best fit your use case.

License