wehub-resource-sync
61 lines
2.9 KiB
Markdown
61 lines
2.9 KiB
Markdown
<!--[metadata]
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title = "Lidar"
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tags = ["Lidar", "3D"]
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thumbnail = "https://static.rerun.io/lidar/caaf3b9531e50285442d17f0bc925eb7c8e12246/480w.png"
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thumbnail_dimensions = [480, 480]
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-->
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Visualize the LiDAR data from the [nuScenes dataset](https://www.nuscenes.org/).
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<picture>
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<img src="https://static.rerun.io/lidar/bcea9337044919c1524429bd26bc51a3c4db8ccb/full.png" alt="">
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<source media="(max-width: 480px)" srcset="https://static.rerun.io/lidar/bcea9337044919c1524429bd26bc51a3c4db8ccb/480w.png">
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<source media="(max-width: 768px)" srcset="https://static.rerun.io/lidar/bcea9337044919c1524429bd26bc51a3c4db8ccb/768w.png">
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<source media="(max-width: 1024px)" srcset="https://static.rerun.io/lidar/bcea9337044919c1524429bd26bc51a3c4db8ccb/1024w.png">
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<source media="(max-width: 1200px)" srcset="https://static.rerun.io/lidar/bcea9337044919c1524429bd26bc51a3c4db8ccb/1200w.png">
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</picture>
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## Used Rerun types
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[`Points3D`](https://www.rerun.io/docs/reference/types/archetypes/points3d)
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## Background
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This example demonstrates the ability to read and visualize LiDAR data from the nuScenes dataset, which is a public large-scale dataset specifically designed for autonomous driving.
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The scenes in this dataset encompass data collected from a comprehensive suite of sensors on autonomous vehicles, including 6 cameras, 1 LIDAR, 5 RADAR, GPS and IMU sensors.
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It's important to note that in this example, only the LiDAR data is visualized. For a more extensive example including other sensors and annotations check out the [nuScenes example](https://www.rerun.io/examples/robotics/nuscenes_dataset).
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## Logging and visualizing with Rerun
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The visualization in this example was created with just the following lines.
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```python
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rr.set_time("timestamp", timestamp=sample_data["timestamp"] * 1e-6) # Setting the time
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rr.log("world/lidar", rr.Points3D(points, colors=point_colors)) # Log the 3D data
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```
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When logging data to Rerun, it's possible to associate it with specific time by using the Rerun's [`timelines`](https://www.rerun.io/docs/concepts/logging-and-ingestion/timelines).
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In the following code, we first establish the desired time frame and then proceed to log the 3D data points.
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## Run the code
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To run this example, make sure you have the Rerun repository checked out and the latest SDK installed:
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```bash
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pip install --upgrade rerun-sdk # install the latest Rerun SDK
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git clone git@github.com:rerun-io/rerun.git # Clone the repository
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cd rerun
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git checkout latest # Check out the commit matching the latest SDK release
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```
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Install the necessary libraries specified in the requirements file:
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```bash
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pip install -e examples/python/lidar
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```
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To experiment with the provided example, simply execute the main Python script:
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```bash
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python -m lidar # run the example
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```
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If you wish to customize it, explore additional features, or save it use the CLI with the `--help` option for guidance:
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```bash
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python -m lidar --help
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```
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