The LidarFrame Representation¶

The core.LidarFrame class is explained in depth in the LidarFrame, which we recommend reading.

We provide example code to aid in understanding.

Query frame for available fields¶

To run the sample code which queries fields from a frame, use the pcap.pcap_query_frame() example:

Linux/macOS

$ python3 -m ouster.sdk.examples.pcap $SAMPLE_DATA_PCAP_PATH query-frame

Windows x64

PS > py -3 -m ouster.sdk.examples.pcap $SAMPLE_DATA_PCAP_PATH query-frame

You can run the above on pcaps containing packets of any type of core.UDPProfileLidar.

You might notice that on a pcap containing dual returns data, you should note that your dtypes will not be consistently uint32_t, as you can tell from the result of running query-frame on dual returns data:

Available fields and corresponding dtype in LidarFrame
RANGE           uint32
RANGE2          uint32
SIGNAL          uint16
SIGNAL2         uint16
REFLECTIVITY    uint8
REFLECTIVITY2   uint8
NEAR_IR         uint16

Projecting into Cartesian Coordinates¶

Let’s plot some points!

If you have a configured sensor, you can plot some points in XYZ using the plot-xyz-points() example:

Linux/macOS

$ python3 -m ouster.sdk.examples.core $SENSOR_HOSTNAME plot-xyz-points

Windows x64

PS > py -3 -m ouster.sdk.examples.core $SENSOR_HOSTNAME plot-xyz-points

That should open a 3D plot of a single frame of your location taken just now by your sensor. You should be able to recognize the contours of the scene around you.

If you don’t have a sensor, you can run the same code with our pcap.pcap_display_xyz_points() pcap example:

Linux/macOS

$ python3 -m ouster.sdk.examples.pcap $SAMPLE_DATA_PCAP_PATH plot-xyz-points --frame-num 84

Windows x64

PS > py -3 -m ouster.sdk.examples.pcap $SAMPLE_DATA_PCAP_PATH plot-xyz-points --frame-num 84

For visualizers which will stream consecutive frames from sensors or pcaps, check out our utilities in Visualizations in 3D.

Working with 2D and 3D Representations Simultaneously¶

The direct correlation between 2D and 3D representations in an Ouster sensor provides a powerful framework for working with the data. As an easy example, you might decide you want to look at only the 3D points within a certain range and from certain azimuth angles.

# 3. Slicing: Limit to the first azimuth_fraction
col_limit = math.floor(info.format.columns_per_frame * azimuth_fraction)
# Standard NumPy slicing [row, col, channel]
xyz_filtered = xyz_filtered[:, 0:col_limit, :]

Since we’d like to filter on azimuth angles, first we first destagger both the 2D and 3D points, so that our columns in the HxW representation correspond to azimuth angle, not timestamp. (See 2D Data Layout: Staggered vs. Destaggered for an explanation on destaggering.)

Then we filter the 3D points xyz_destaggered by comparing the range measurement to range_min, which we can do because there is a 1:1 correspondence between the columns and rows of the destaggered representations of xyz_destaggered and range_destaggered. (Similarly, there would be a 1:1 correspondence between the staggered representations xyz and range, where the columns correspond with timestamp).

Finally, we select only the azimuth columns we’re interested in. In this case, we’ve arbitrarily chosen the first 270 degrees of rotation.

If you have a configured sensor, you can run this code with an example:

Linux/macOS

$ python3 -m ouster.sdk.examples.core $SENSOR_HOSTNAME filter-3d-by-range-and-azimuth

Windows x64

PS > py -3 -m ouster.sdk.examples.core $SENSOR_HOSTNAME filter-3d-by-range-and-azimuth