Multiscan Pointcloud Height & Width Issues: A Deep Dive

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Multiscan Pointcloud Height & Width Issues: A Deep Dive

Hey guys! Let's dive into a common head-scratcher when working with multiscan structured pointclouds from SICK lidar, specifically the SICKAG and sick_scan_xd packages. I understand that the pointcloud's structure might seem a little confusing at first. For instance, the segments aren't necessarily ordered, and the points themselves don't appear to be fixed in place. The main issue we'll tackle is the height and width values reported in the pointcloud header. When you're trying to integrate this data into systems like LIO-SAM (LiDAR-inertial odometry and mapping with the Semantic Assisted Map), getting these values right is crucial for accurate processing. Let's break down the problem and see how we can solve it.

Understanding the Problem: Pointcloud Structure and Header Values

First off, let's talk about what we're seeing. The pointcloud data coming from your /points topic has a specific structure. You're getting everything published under the same fields, which can indeed be confusing. The core problem lies in the height and width values within the header of your pointcloud messages. These values define the dimensions of your pointcloud data, and when they aren't accurate, it can mess up the way your other software interprets the pointcloud, especially if it relies on a specific organization of the points.

Pointcloud Configuration

You've provided your pointcloud configuration, which is a great starting point:

<param name="cloud_lio_sam" type="string" value="coordinateNotation=3 updateMethod=0 
fields=x,y,z,i,ring,ts echos=0 layers=1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16 reflectors=0,1 
infringed=0,1 rangeFilter=0,999,0 topic=/points frameid=lidar_link publish=1"/>

This configuration sets up how your pointcloud data is structured. It defines the fields you're using (x, y, z, i, ring, ts), the number of echos and layers, and other parameters. Essentially, this is the blueprint for how the data is organized. The parameters related to echos and layers might influence the eventual height/width calculations, so let's keep that in mind.

Header Analysis

Next, you shared the header information from your /points topic:

header:
  stamp:
    sec: 1764831737
    nanosec: 817435979
  frame_id: lidar_link
height: 1
width: 10800
fields:
- name: x
  offset: 0
  datatype: 7
  count: 1
- name: y
  offset: 4
  datatype: 7
  count: 1
- name: z
  offset: 8
  datatype: 7
  count: 1
- name: i
  offset: 12
  datatype: 7
  count: 1
- name: ts
  offset: 16
  datatype: 7
  count: 1
- name: ring
  offset: 20
  datatype: 1
  count: 1
is_bigendian: false
point_step: 21
row_step: 226800

The header is where the problem lies. You have height: 1 and width: 10800. This is the crux of the issue. The reported height of 1 suggests that the data is treated as a single row of points. While the width of 10800 indicates there are a lot of points in that single row. This might be accurate, or it might be a simplification based on how the SICK lidar data is organized internally. In other words, this configuration can affect how LIO-SAM processes the data because it expects a more structured pointcloud, like rows and columns. In a perfect world, height and width would give you a good way to estimate the pointcloud dimensions (e.g. number of rows and number of points in each row). With this, we have to look deeper to see how the lidar data is actually arranged.

Getting Accurate Height and Width Values

So, how can we improve the height and width values for your multiscan structured pointcloud? It all comes down to understanding the organization of the data coming from your specific SICK lidar model and the sick_scan_xd driver. Here's a breakdown of possible solutions:

1. Investigate the sick_scan_xd Driver

  • Driver Documentation: The first place to look is the documentation for the sick_scan_xd driver. There might be parameters or settings that influence how the pointcloud data is structured and how the height and width are calculated. Look for any options related to the number of scan lines, layers, or rings. Guys, reading the fine print is crucial.
  • Source Code: If the documentation isn't clear, you can dig into the driver's source code. Look for the parts of the code that create and populate the pointcloud messages. Find out how the height and width values are calculated. See if these calculations are correct for your use case. In the code, you'll likely find the logic that determines the size of the pointcloud based on the sensor's configuration and the received data.

2. Understanding Lidar Specifications

  • Number of Layers: Your lidar likely has a certain number of scan layers or rings. If you know the number of layers, and if each layer is treated as a row, then you could potentially set the height to the number of layers. Now, we're not talking about just any lidar, but the SICK lidar you're using. So you should check the specifications for your exact model. Then, the width would be the average number of points per layer. This number may vary depending on the sensor's configuration and operating mode. For example, if it emits 16 layers, height might be 16.
  • Points per Revolution: Check the datasheet of your specific lidar model. Find the number of points it emits per revolution or scan. Knowing the number of points per scan and the number of layers will help you calculate the width and height. You can then manually set the values, or you can adjust the driver to determine them. This will make your pointcloud more structured.

3. Custom Processing and Reshaping

  • Pointcloud Processing: You might need to add a processing step in your ROS pipeline. This node will subscribe to the /points topic, analyze the raw pointcloud data, and then reshape it into a more structured form. For example, you can calculate the height and width based on the number of points and the number of layers. You could then publish a new pointcloud with the correct header values.
  • Data Conversion: Consider converting the pointcloud data into a different format if the default one isn't compatible with LIO-SAM. For example, you could convert the pointcloud into a format that LIO-SAM expects (e.g., organized point clouds where the points are ordered in rows and columns).

4. Adjustments in the LIO-SAM Configuration

  • Parameter Tuning: The cloud_lio_sam parameter you defined might need adjustment. Look for options related to the pointcloud's dimensions or the expected data structure. If LIO-SAM has parameters that define the expected height and width, you can modify these values accordingly. LIO-SAM has been designed for a variety of sensors, so there might be parameters that can be adjusted to match the lidar's pointcloud structure. Sometimes, a little tweaking can go a long way.
  • Code Modifications: As a last resort, if you cannot resolve the issue through configuration, you might need to make small modifications to the LIO-SAM code itself. This is only recommended if you understand the internal workings of the algorithm and how it processes pointcloud data. Adapt the parts that handle the pointcloud's dimensions to accommodate the structure from the SICK lidar.

Adapting LIO-SAM to Run with SICK Lidar

Now, let's talk about getting LIO-SAM to work with your SICK lidar. This is where the understanding of the height and width values becomes super important. Let's explore the core principles for success.

1. Understanding LIO-SAM's Pointcloud Expectations

  • Structured Pointclouds: LIO-SAM generally works well with structured pointclouds. That means the points are organized in a grid-like structure (rows and columns). If the data from your SICK lidar isn't organized this way, you'll need to transform the data, or you will have to configure LIO-SAM to accept the sensor’s structure.
  • Key Parameters: Identify the parameters within LIO-SAM that specify the height and width of the input pointclouds. These parameters are essential for LIO-SAM to process the data correctly. Without this, the software will not process the pointcloud correctly.

2. Matching the LIO-SAM Configuration

  • ROS Parameters: The easiest way to configure LIO-SAM is through ROS parameters. You'll need to modify the launch files or configuration files to set the height and width to the correct values based on your lidar's specifications and the sick_scan_xd driver's output. Make sure that you are pointing to the correct topic, /points in your case, and configure the parameters of the cloud. Use the configuration files to adjust parameters, such as the cloud_lio_sam one.
  • Topic Remapping: You can remap the /points topic to the expected topic name in LIO-SAM if necessary. This helps to connect your lidar's output to the correct input of the LIO-SAM node.

3. Data Preprocessing

  • Preprocessing Node: Before sending the pointcloud data to LIO-SAM, you might need a pre-processing node. This node will perform the following:
    • Reshaping: Reshape the data if the height and width values in your pointcloud header are not accurate. Calculate the correct dimensions based on the sensor's specifications.
    • Filtering: Apply necessary filtering to remove noise or irrelevant points. This will improve the quality of the pointclouds that you are processing.
    • Data Conversion: Convert the data into the expected format. Be sure that the format is correct to avoid future issues.
  • Example (Conceptual): The pre-processing node would subscribe to /points, calculate the proper height and width based on the number of scan layers, and then republish the pointcloud with the updated header. The pre-processing node would filter the pointclouds to make the structure right before passing it to LIO-SAM.

4. Testing and Calibration

  • Calibration Data: Start by testing the system using calibration data or recorded pointclouds. Run the LIO-SAM with these datasets to ensure everything works correctly. This gives you the basis for understanding how your changes affect the system.
  • Real-time Testing: Then, test the system in real-time. Drive your robot or vehicle around while LIO-SAM is running and monitor the output. Look for any errors or unexpected behaviors. Check the odometry and mapping results to ensure that they are accurate.

5. Troubleshooting and Iteration

  • Debugging: If you encounter any problems, carefully examine the ROS logs for error messages. These messages will provide insights into what is going wrong.
  • Iterative Refinement: Modify your configuration, pre-processing, or even the LIO-SAM code if necessary. Test the system after each change, and continue iterating until the results are satisfactory.

Conclusion: Making it Work

So, to wrap things up, getting the height and width right in your multiscan structured pointcloud from the SICK lidar is key for making LIO-SAM work properly. It involves understanding the sensor's specifications, understanding the structure of the pointcloud, and possibly adding pre-processing steps. Start by understanding how the sick_scan_xd driver works, look closely at your lidar's specs, and make sure that the height and width values are correct. Once you've got this, you can then move on to LIO-SAM and configure it to work with your lidar's pointcloud format. By applying these steps, you will make your navigation and mapping tasks work like a charm, so you can start creating detailed maps or having a reliable odometry! Good luck, and happy coding!