LiDAR Control Module#

Access LiDAR sensor data from the Unitree Go2 robot or the Mujoco Simulator. Users are recommended to interact with LIDARModule when PointCloud2 data is needed.

If the provided modules do not sufficiently meet your needs, you can bypass them and use an alternative approach. You can also submit a feature request on the main GitHub page, and it will be considered for future updates.

class go2.modules.lidar.lidar_module.LIDARModule(publish_hz: int = 10)[source]#

Bases: DogModule

LIDARModule provides a simple API for:

  • Receiving decoded PointCloud2 structures as xyz-(optionally intensity) numpy arrays

Users should not access or construct this class directly. Rather, they should access it through the Go2Controller instance.

Parameters:

publish_hz (int) –

The publishing frequency (in Hz) of decoded point cloud frames. This dictates how many times per second the registered callback is triggered. A lower publishing rate results in a longer point accumulation time per frame, yielding a denser point cloud with greater field-of-view (FOV) coverage, but introduces higher delay between publishings. (Although, the data transfer will always be an O(1) pointer-swap internally). Must be in the range [5, 100]. Default is 10.

  • When running in HardwareType.Native mode, publish_hz does not control the actual publish rate of the physical LIDAR sensor. You must configure that rate manually via the LIDAR’s own driver/firmware settings. In this mode, publish_hz only governs how frequently this wrapper internally polls for new data over IPC from the ROS2 publisher node to this receiver.

  • When running in HardwareType.Virtual mode, publish_hz does matter, since we are mocking the real LIDAR and fully control when it publishes data.

Important

When executing on HardwareType.Native this module launches ROS2 nodes. In such a case, it is critical that students ALWAYS call Go2Controller.safe_shutdown() after normal (error free) script exit.

register_decoded_pointcloud_callback(callback: Callable[[int, ndarray], None]) → None[source]#

Register a callback to receive decoded PointCloud2 data. The callback is triggered whenever a new raw point cloud sample is received via Iceoryx2.

Parameters:

callback (Callable[[int, np.ndarray], None]) –

A function to be called with:

  • timestamp (int): The source timestamp in nanoseconds.

  • points (np.ndarray): A Fortran-contiguous float32 array of shape (3, N)

    for [x, y, z] or (4, N) if intensity is supported [x, y, z, intensity].

Important

  • Shared Views:

    During each cycle, all subscribers receive views of the same underlying data. This is done for performance. Modifying data through the returned view is unsafe, as it affects the shared data. If you need to modify the data, create a copy first.

  • Cache Locality & Iteration:

    Because the array is Fortran-contiguous, the data is laid out column-by-column in memory (e.g., x0, y0, z0, x1, y1, z1…). This means all coordinates (plus intensity) for a single point are tightly packed together. Therefore, column-major iteration should be greatly prefered over row-major iteration to maximize cache efficiency. If you need a C-contiguous array (row-major) you can use use numpy.ascontiguousarray(array).

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