isaac_ros_bi3d_freespace

Source code on GitHub.

Quickstart

Set Up Development Environment

1. Set up your development environment by following the instructions in getting started.

2. Clone isaac_ros_common under ${ISAAC_ROS_WS}/src.

   cd ${ISAAC_ROS_WS}/src && \
      git clone https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_common.git
   

3. (Optional) Install dependencies for any sensors you want to use by following the sensor-specific guides.

   Warning

   We strongly recommend installing all sensor dependencies before starting any quickstarts. Some sensor dependencies require restarting the Isaac ROS Dev container during installation, which will interrupt the quickstart process.

Download Quickstart Assets

1. Download quickstart data from NGC:

   Make sure required libraries are installed.

   sudo apt-get install -y curl tar
   

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

   NGC_ORG="nvidia"
   NGC_TEAM="isaac"
   NGC_RESOURCE="isaac_ros_assets"
   NGC_VERSION="isaac_ros_bi3d_freespace"
   NGC_FILENAME="quickstart.tar.gz"
   
   REQ_URL="https://api.ngc.nvidia.com/v2/resources/$NGC_ORG/$NGC_TEAM/$NGC_RESOURCE/versions/$NGC_VERSION/files/$NGC_FILENAME"
   
   mkdir -p ${ISAAC_ROS_WS}/isaac_ros_assets/${NGC_VERSION} && \
       curl -LO --request GET "${REQ_URL}" && \
       tar -xf ${NGC_FILENAME} -C ${ISAAC_ROS_WS}/isaac_ros_assets/${NGC_VERSION} && \
       rm ${NGC_FILENAME}
   

2. Download a pre-trained Bi3D model:

   mkdir -p ${ISAAC_ROS_WS}/isaac_ros_assets/models/bi3d_proximity_segmentation && \
   cd ${ISAAC_ROS_WS}/isaac_ros_assets/models/bi3d_proximity_segmentation && \
   wget 'https://api.ngc.nvidia.com/v2/models/nvidia/isaac/bi3d_proximity_segmentation/versions/2.0.0/files/featnet.onnx' &&
   wget 'https://api.ngc.nvidia.com/v2/models/nvidia/isaac/bi3d_proximity_segmentation/versions/2.0.0/files/segnet.onnx'
   

Build isaac_ros_bi3d_freespace

Binary PackageBuild from Source

1. Launch the Docker container using the run_dev.sh script:

   cd ${ISAAC_ROS_WS}/src/isaac_ros_common && \
   ./scripts/run_dev.sh
   

2. Install the prebuilt Debian package:

   sudo apt-get install -y ros-humble-isaac-ros-bi3d-freespace
   

Run Launch File

1. Convert the .onnx model files to TensorRT engine plan files:

   x86_64Jetson

   /usr/src/tensorrt/bin/trtexec --saveEngine=${ISAAC_ROS_WS}/isaac_ros_assets/models/bi3d_proximity_segmentation/featnet.plan \
   --onnx=${ISAAC_ROS_WS}/isaac_ros_assets/models/bi3d_proximity_segmentation/featnet.onnx --int8 &&
   /usr/src/tensorrt/bin/trtexec --saveEngine=${ISAAC_ROS_WS}/isaac_ros_assets/models/bi3d_proximity_segmentation/segnet.plan \
   --onnx=${ISAAC_ROS_WS}/isaac_ros_assets/models/bi3d_proximity_segmentation/segnet.onnx --int8
   

   Note

   The engine plans generated using the x86_64 commands will also work on Jetson, but performance will be reduced.

RosbagRealSense CameraHawk Camera

1. Continuing inside the Docker container, install the following dependencies:

   sudo apt-get install -y ros-humble-isaac-ros-examples ros-humble-isaac-ros-bi3d
   

2. Run the following launch file to spin up a demo of this package using the quickstart rosbag:

   ros2 launch isaac_ros_examples isaac_ros_examples.launch.py \
   launch_fragments:=bi3d,bi3d_freespace \
   interface_specs_file:=${ISAAC_ROS_WS}/isaac_ros_assets/isaac_ros_bi3d_freespace/rosbag_quickstart_interface_specs.json \
   featnet_engine_file_path:=${ISAAC_ROS_WS}/isaac_ros_assets/models/bi3d_proximity_segmentation/featnet.plan \
   segnet_engine_file_path:=${ISAAC_ROS_WS}/isaac_ros_assets/models/bi3d_proximity_segmentation/segnet.plan \
   max_disparity_values:=10
   

3. Open a second terminal inside the Docker container:

   cd ${ISAAC_ROS_WS}/src/isaac_ros_common && \
   ./scripts/run_dev.sh
   

4. Run the rosbag file to simulate image streams:

   ros2 bag play -l ${ISAAC_ROS_WS}/isaac_ros_assets/isaac_ros_bi3d_freespace/quickstart.bag
   

Visualize Results

1. Open a new terminal inside the Docker container:

   cd ${ISAAC_ROS_WS}/src/isaac_ros_common && \
      ./scripts/run_dev.sh
   

2. Visualize the occupancy grid in RViz.

   Start RViz:

   rviz2
   

   In the left pane, change Fixed Frame to base_link.

   In the left pane, click the Add button, then select By topic followed by Map to add the occupancy grid.

   

Try More Examples

To continue your exploration, check out the following suggested examples:

• Tutorial with Isaac Sim

API

Usage

ros2 launch isaac_ros_bi3d_freespace isaac_ros_freespace_segmentation.launch.py base_link_frame:=<"name of base link"> camera_frame:=<"name of camera frame"> f_x:=<"focal length in pixels in x dimension"> f_y:=<"focal length in pixels in y dimension"> grid_width:=<"desired grid width"> grid_height:=<"desired grid height"> grid_resolution:=<"desired grid resolution">

ROS Parameters

ROS Parameter	Type	Default	Description
base_link_frame	std::string	base_link	The name of the tf2 frame attached to the robot base
camera_frame	std::string	camera	The name of the tf2 frame attached to the camera
f_x	double	0.0	The focal length in pixels in x dimension
f_y	double	0.0	The focal length in pixels in y dimension
grid_width	int	100	The width of the output occupancy grid, in number of cells
grid_height	int	100	The height of the output occupancy grid, in number of cells
grid_resolution	double	0.01	The resolution of the output occupancy grid, in meters per cell

ROS Topics Subscribed

ROS Topic	Interface	Description
freespace_segmentation/mask_in	stereo_msgs/DisparityImage	The input disparity image, with pixels corresponding to ground labeled as 0

Warning

Limitation: For all input images, both the height and width must be an even number of pixels.

ROS Topics Published

ROS Topic	Interface	Description
freespace_segmentation/occupancy_grid	nav_msgs/OccupancyGrid	The output occupancy grid, with cells marked as 0 if free