Ouster OS1 LiDAR Examples

This page contains tutorials for running nvblox with the Ouster OS1 3D LiDAR sensor.

Note

This tutorial only supports running from rosbags, specifically the DOALS dataset. For live OS1 sensor integration, you will need to modify the provided launch file to include a sensor driver.

Prerequisites

These are the steps common to running all examples on the Ouster OS1.

1. Complete the Getting Started.

2. Complete the nvblox quickstart.

OS1 Example

This example runs nvblox-based reconstruction from Ouster OS1 3D LiDAR data recorded in a rosbag.

1. Download the DOALS dataset from NGC:

   Make sure required libraries are installed.

   sudo apt-get install -y curl jq tar unzip
   

   Then, run these commands to download the asset from NGC:

   NGC_ORG="nvidia"
   NGC_TEAM="isaac"
   PACKAGE_NAME="isaac_ros_nvblox"
   NGC_RESOURCE="isaac_ros_nvblox_assets"
   NGC_FILENAME="UrbanDynamicObjectsLidarDataset_hauptgebaeude.zip"
   MAJOR_VERSION=5
   MINOR_VERSION=0
   VERSION_REQ_URL="https://api.ngc.nvidia.com/v2/resources/$NGC_ORG/$NGC_TEAM/$NGC_RESOURCE/versions"
   AVAILABLE_VERSIONS=$(curl -s \
       -H "Accept: application/json" "$VERSION_REQ_URL")
   LATEST_VERSION_ID=$(echo $AVAILABLE_VERSIONS | jq -r "
       .recipeVersions[]
       | .versionId as \$v
       | \$v | select(test(\"^\\\\d+\\\\.\\\\d+\\\\.\\\\d+$\"))
       | split(\".\") | {major: .[0]|tonumber, minor: .[1]|tonumber, patch: .[2]|tonumber}
       | select(.major == $MAJOR_VERSION and .minor <= $MINOR_VERSION)
       | \$v
       " | sort -V | tail -n 1
   )
   if [ -z "$LATEST_VERSION_ID" ]; then
       echo "No corresponding version found for Isaac ROS $MAJOR_VERSION.$MINOR_VERSION"
       echo "Found versions:"
       echo $AVAILABLE_VERSIONS | jq -r '.recipeVersions[].versionId'
   else
       mkdir -p ${ISAAC_ROS_WS}/isaac_ros_assets && \
       FILE_REQ_URL="https://api.ngc.nvidia.com/v2/resources/$NGC_ORG/$NGC_TEAM/$NGC_RESOURCE/\
   versions/$LATEST_VERSION_ID/files/$NGC_FILENAME" && \
       curl -LO --request GET "${FILE_REQ_URL}" && \
       unzip -o ${NGC_FILENAME} -d ${ISAAC_ROS_WS}/isaac_ros_assets && \
       rm ${NGC_FILENAME}
   fi
   

   Note

   This dataset is mirrored from the DOALS dataset and redistributed under its Creative Commons license. Review the license terms on the dataset page before use. Cite the original authors when using the dataset in your research:

   Patrick Pfreundschuh, Hubertus F.C. Hendrikx, Victor Reijgwart, Renaud Dubé, Roland Siegwart, Andrei Cramariuc. Dynamic Object Aware LiDAR SLAM based on Automatic Generation of Training Data. In IEEE International Conference on Robotics and Automation (ICRA), 2021. [ IEEE | ArXiv ]

2. Convert the ROS1 bag to ROS2 format:

   cd $ISAAC_ROS_WS/isaac_ros_assets
   pip install rosbags --break-system-packages
   rosbags-convert --src hauptgebaeude/sequence_1/2020-02-20-11-58-45.bag --dst hauptgebaeude_ros2
   

   Note

   The --break-system-packages option is required on recent versions of Python-pip if you are not using a virtual environment.

   Note

   For more details, see the Rosbags Conversion Guide.

3. Activate the Isaac ROS environment:

   isaac-ros activate
   

4. Run the OS1 example with your converted rosbag:

   ros2 launch nvblox_examples_bringup os1_example.launch.py \
       rosbag:=$ISAAC_ROS_WS/isaac_ros_assets/hauptgebaeude_ros2 mode:=dynamic
   

Note

The OS1 example supports mode:=static (default) and mode:=dynamic arguments. As the DOALS dataset contains groups of people walking around, the dynamic mode is recommended to reconstruct the scene. Unlike camera-based examples, LiDAR does not support people segmentation or detection modes.

Note

The DOALS dataset contains motion distorted LiDAR data (i.e. the sensor was moving significantly during scan recordings). To increase the quality of the reconstruction, nvblox is running CUDA-accelerated motion compensation. Each point in the pointcloud is motion compensated by approximating the sensor frame at the time of the point’s acquisition with an interpolation of the sensor pose between scan start and end. The use_lidar_motion_compensation parameter (default is true) can be used to enable or disable motion compensation.