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You are correct. The data you get from your sensors are in their own frame of reference. For distance / range measurements, its often a good idea to have them in the frame of reference of your robot; usually base_link. Fortunately ROS provides the tf library for this purpose. You can find more information here.

I highly recommend you go through their tutorials but the one you are looking for is a tf listener python / C++.

Once you have this transform, you can apply it to your scan. Unfortunately laserscans aren't very easy to work with and I'd suggest you convert your scan to a PointCloud2 msg and use the features PCL has to offer.

You are correct. The data you get from your sensors are in their own frame of reference. For distance / range measurements, its often a good idea to have them in the frame of reference of your robot; usually base_link. Fortunately ROS provides the tf library for this purpose. You can find more information here.

I highly recommend you go through their tutorials but the one you are looking for is a tf listener python / C++.

Once you have this transform, you can apply it to your scan. Unfortunately laserscans aren't very easy to work with and I'd suggest you convert your scan to a PointCloud2 msg and use the features PCL has to offer.

<node pkg="pointcloud_to_laserscan" type="laserscan_to_pointcloud_node" name="laserscan_to_pointcloud">
    <remap from="scan_in" to="/scan"/>
    <remap from="cloud" to="/hokuyo_cloud"/>
</node>

void CloudCallback(const sensor_msgs::PointCloud2ConstPtr &cloud_in) 
{
// copy point cloud
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
pcl::fromROSMsg(*cloud_in, *cloud);

// Transform point cloud
try {
  transform_stamped = tf_buffer.lookupTransform("base_link",
                                            cloud_in->header.frame_id,
                                            ros::Time(0),
                                            ros::Duration(1));
} catch (tf2::TransformException &ex) {
  ROS_ERROR("%s", ex.what());
  return;
}

// apply transform to point cloud
Eigen::Affine3d pc_to_base_link = tf2::transformToEigen(transform_stamped);
pcl::transformPointCloud(*cloud, *cloud,
                         pc_to_base_link);

/* 
Use cloud as you normally would 
*/

// convert back to ROS
sensor_msgs::PointCloud2 cloud_out;
pcl::toROSMsg(*cloud, cloud_out);
cloud_out.header.frame_id = cloud_in->header.frame_id;
cloud_out.header.stamp = ros::Time::now();

// publish
publisher.publish(cloud_out);

}

You are correct. The data you get from your sensors are in their own frame of reference. For distance / range measurements, its often a good idea to have them in the frame of reference of your robot; usually base_link. Fortunately ROS provides the tf library for this purpose. You can find more information here.

I highly recommend you go through their tutorials but the one you are looking for is a tf listener python / C++.

Once you have this transform, you can apply it to your scan. Unfortunately laserscans aren't very easy to work with and I'd suggest you convert your scan to a PointCloud2 msg and use the features PCL has to offer.

EDIT: Based on your LiDAR scan msg type, here are my suggestions:

Use the laserscan_to_pointcloud package to convert your laserscan to PointCloud2 type. You can just include this into your launch file:

<node pkg="pointcloud_to_laserscan" type="laserscan_to_pointcloud_node" name="laserscan_to_pointcloud">
    <remap from="scan_in" to="/scan"/>
to="/<your scan topic>"/>
    <remap from="cloud" to="/hokuyo_cloud"/>
to="/<your cloud topic>"/>
</node>

Then, in your LiDAR callback, convert from ROS type to PCL type, lookup tf and apply it to your data. This is standard pipeline

void CloudCallback(const sensor_msgs::PointCloud2ConstPtr &cloud_in) 
{
// copy point cloud
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
pcl::fromROSMsg(*cloud_in, *cloud);

// Transform point cloud
try {
  transform_stamped = tf_buffer.lookupTransform("base_link",
                                            cloud_in->header.frame_id,
                                            ros::Time(0),
                                            ros::Duration(1));
} catch (tf2::TransformException &ex) {
  ROS_ERROR("%s", ex.what());
  return;
}

// apply transform to point cloud
Eigen::Affine3d pc_to_base_link = tf2::transformToEigen(transform_stamped);
pcl::transformPointCloud(*cloud, *cloud,
                         pc_to_base_link);

/* 
Use cloud as you normally would 
*/

// convert back to ROS
sensor_msgs::PointCloud2 cloud_out;
pcl::toROSMsg(*cloud, cloud_out);
cloud_out.header.frame_id = cloud_in->header.frame_id;
cloud_out.header.stamp = ros::Time::now();

// publish
publisher.publish(cloud_out);

}

You are correct. The data you get from your sensors are in their own frame of reference. For distance / range measurements, its often a good idea to have them in the frame of reference of your robot; usually base_link. Fortunately ROS provides the tf library for this purpose. You can find more information here.

I highly recommend you go through their tutorials but the one you are looking for is a tf listener python / C++.

Once you have this transform, you can apply it to your scan. Unfortunately laserscans aren't very easy to work with and I'd suggest you convert your scan to a PointCloud2 msg and use the features PCL has to offer.

EDIT: Based on your LiDAR scan msg type, here are my suggestions:

Use the laserscan_to_pointcloud package to convert your laserscan to PointCloud2 type. You can just include this into your launch file:

<node pkg="pointcloud_to_laserscan" type="laserscan_to_pointcloud_node" name="laserscan_to_pointcloud">
    <remap from="scan_in" to="/<your scan topic>"/>
    <remap from="cloud" to="/<your cloud topic>"/>
</node>

Then, in your LiDAR callback, convert from ROS type to PCL type, lookup tf and apply it to your data. This is standard a simple pipeline

void CloudCallback(const sensor_msgs::PointCloud2ConstPtr &cloud_in) 
{
// copy point cloud
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
pcl::fromROSMsg(*cloud_in, *cloud);

// Transform point cloud
try {
  transform_stamped = tf_buffer.lookupTransform("base_link",
                                            cloud_in->header.frame_id,
                                            ros::Time(0),
                                            ros::Duration(1));
} catch (tf2::TransformException &ex) {
  ROS_ERROR("%s", ex.what());
  return;
}

// apply transform to point cloud
Eigen::Affine3d pc_to_base_link = tf2::transformToEigen(transform_stamped);
pcl::transformPointCloud(*cloud, *cloud,
                         pc_to_base_link);

/* 
Use cloud as you normally would 
*/

// convert back to ROS
sensor_msgs::PointCloud2 cloud_out;
pcl::toROSMsg(*cloud, cloud_out);
cloud_out.header.frame_id = cloud_in->header.frame_id;
cloud_out.header.stamp = ros::Time::now();

// publish
publisher.publish(cloud_out);

}