Voxeland is a library for semantic mapping in voxelized worlds. It supports both dense semantics and instance-aware mapping, and is able to provide uncertainty estimations for the category labels and instance segmentation per voxel. It is designed to be used through a ROS2 node, and get new observations on-line through topics.
It is based on the excellent Bonxai library, and relies on much of its code for storing and manipulating volumetric data.
TODO
Move to your colcon workspace and run:
git clone --recurse-submodules git@github.com:MAPIRlab/Voxeland.git src/voxeland
This will download the code for voxeland and some third party libraries. You will also need the segmentation_msgs package, available here.
Once you have everything downloaded, compile with colcon build --symlink-install as usual.
First, run the instance segmentation network (e.g., Mask R-CNN). A ROS2 implementation can be found at: Detectron2. To run detectron2 framework:
ros2 run detectron2 detectron2_ros_node
Next, run the robot perception node as follows:
ros2 launch voxeland_robot_perception robot_perception_node.py
Finally, execute Voxeland to start the mapping session:
ros2 launch voxeland bonxai_mapping.launch.xml
(Now, everything is ready for the semantic mapping session, as soon as you play your dataset.)