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I'm currently facing an issue while working on my robotics project. I have set up a dynamic transform for Lidar data from Map-> odam-> base_footprint->base_link. Following this, I run the ydlidar_ros_driver using the Ydlidar_launch.py file, and then I initiate the slam_toolbox using the online_async_launch.py file.

While the setup successfully creates a transform, I'm encountering a problem with the continuous creation of the map. The map doesn't seem to be updating as expected. I'm using the YDLidar X2.

Has anyone experienced a similar issue or could provide guidance on how to troubleshoot this problem? Any insights or suggestions would be greatly appreciated.

This code that i used to generates transforms for the coordinate frames "map" to "odom," "odom" to "base_footprint," and "base_footprint" to "base_link" using lidar data.

class LidarTransformPublisher(Node): def __init__(self): super().__init__('lidar_transform_publisher') self.tf_broadcaster = tf2_ros.TransformBroadcaster(self) # Subscribe to lidar data self.create_subscription(LaserScan, '/scan', self.lidar_callback, rclpy.qos.qos_profile_sensor_data) def lidar_callback(self, lidar_data): # Extract relevant information from lidar data ranges = lidar_data.ranges angle_increment = lidar_data.angle_increment # Assuming lidar_data provides an angle for each range theta = lidar_data.angle_min for range_value in ranges: # Calculate x and y based on range and current angle x = range_value * math.cos(theta) y = range_value * math.sin(theta) # Create dynamic transformations map_to_odom_transform = self.create_transform('map', 'odom', x, y, theta) odom_to_base_footprint_transform = self.create_transform('odom', 'base_footprint', 2 * x, 2 * y, theta) base_footprint_to_base_link_transform = self.create_transform('base_footprint', 'base_link', 3 * x, 3 * y, theta) # Send dynamic transformations self.tf_broadcaster.sendTransform(map_to_odom_transform) self.tf_broadcaster.sendTransform(odom_to_base_footprint_transform) self.tf_broadcaster.sendTransform(base_footprint_to_base_link_transform) # Increment the angle for the next range theta += angle_increment def create_transform(self, parent_frame, child_frame, x, y, theta): transform = TransformStamped() transform.header.stamp = self.get_clock().now().to_msg() transform.header.frame_id = parent_frame transform.child_frame_id = child_frame transform.transform.translation.x = x transform.transform.translation.y = y transform.transform.translation.z = 0.0 transform.transform.rotation.x = 0.0 transform.transform.rotation.y = 0.0 transform.transform.rotation.z = math.sin(theta / 2.0) transform.transform.rotation.w = math.cos(theta / 2.0) return transform

Thank you!

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Have you tried using static transforms first?

If I understand your code as written, you are publishing new transforms for the location of the lidar based on values from the scan. This is incorrect. The transforms should be derived from your robot's physical configuration. I don't see a transform showing how the laser scanner is mounted relative to the base_link.

Second, the odom --> base_link transform should be derived from your robot's odometry, and you may need a specific driver for your specific hardware platform.

Third, the SLAM toolbox should be publishing the map --> odom transform.

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  • $\begingroup$ Initially, I tried a static transform config (odam->base_footprint->base_footprint->base_link) with ydlidar and slam_toolbox, but I'm getting a "no map received" error. Then, I switched to a dynamic transform (map->odam->base_footprint->base_link->laser_frame), and this time I got a map, but it's not updating continuously. Just to clarify, I haven't tested it with a physical robot yet; I'm only trying to get a map with Lidar data. Any ideas on how to fix these issues and get a continuous map update? $\endgroup$ Commented Jan 17, 2024 at 4:57
  • $\begingroup$ If you only have a lidar, then your TF tree should be very simple (and static). 1. Since you have no robot, you have no footprint, so you don't need any base_footprint frame. 2. base_link --> laser_frame can be an identity transform, or you could put in a little offset based on where the laser is when the sensor is sitting on a table. 3. You don't have odometry, so odom --> base_link is also an identity transform. 4. SLAM toolbox publishes map --> odom. $\endgroup$ Commented Jan 17, 2024 at 5:33
  • $\begingroup$ Thanks for your earlier reply. I've tried creating an 'odom' to 'base_link' static transform. I've also started the Ydlidar_ros_driver package and initiated the slam_toolbox online_async_launch file. However, I'm facing an issue. The transform chain 'odom' -> 'base_link' -> 'laser_frame' is created, but I'm not receiving any data on the '/map' topic. I'm getting a "No map received" warning. how to solve this $\endgroup$ Commented Jan 17, 2024 at 10:51
  • $\begingroup$ Is your lidar publishing the same topic as the SLAM toolbox is subscribed to? You can use the rqt_graph and ros2 topic info tools to check that your data is flowing the way you expect it to. Also, since you don't have odometry, you may need to move the lidar slowly to build your map. In fact, you should receive the first map without moving the lidar at all, though it takes a beat after launching the stack for the map to become available. $\endgroup$ Commented Jan 17, 2024 at 17:46
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  1. I would check if you are using the default mapper_params_online_async.yaml file which the launch file you mentioned calls (it is in root, but you should build locally or modify in /opt/ros/distro/share/slam_toolbox/cfg/mapper_params_online...)

Usually is set small width/height for mapping, different lidar range (small) which is only suitable for turtlebot tutorial.

  1. Additionally if changing parameters doesn't work, check if map_server is active(sometimes it is not auto started by launch file if not defined):

A) Check:

ros2 lifecycle list <map_server_node_name>

B) Get Status

ros2 lifecycle get map_server (or proper node name)

C) Activate if not active

ros2 lifecycle set map_server activate

  1. If the issue is your robot tf, just visualize if is correct or not:

    ros2 run tf2_tools view_frames

Then visualize the tree:

evince frames.pdf

Double check the transform chain from Odom to base_link:

ros2_run tf2_ros tf2_echo <from_frame> <to_frame>
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