This package creates a rapid reliable waypoint navigation architecture using ICP localization to a prior map. It subscribes to lidar scans and odometry data to register location in a given map. Using the move_base navigation stack, we built a map server to run with robust 3D localization.
The user should build a reference map (point cloud) as a .pcd file and define an initial pose in the reference map. We recommend using hdl_graph_slam.
Released under BSD 3-Clause license.
Author: Erik Pearson
Maintainer: Erik Pearson, [email protected]
| waypoint navigation in a lab space |
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Install ICP localization in your workspace and check to confirm it builds. Recommended to build in release mode for performance (catkin config -DCMAKE_BUILD_TYPE=Release). Then install this package for waypoint navigation:
# in your source folder 'src'
git clone https://github.com/RobustFieldAutonomyLab/waypoint_navigation.gitWe also recommend installing hdl_graph_slam to quickly build reference maps.
You can launch the program in simulation with:
roslaunch waypoint_navigation waypoint_navigation_simulation.launchGoal positions are in the params sub folder in the format [pose x, pose y, orientation z, orientation w, pose x2, pose y2, orientation z2, orientation w2]
For use on a robot, we recommend using waypoint_navigation.launch, which uses icp_node.launch for localization parameters and goals.yaml for waypoint positions.
The pcd_filepath parameter in the icp_node.launch file should point to the location where you stored your refrence map (pointcloud) in the .pcd format.
The configuration for iterative closest point is split into three .yaml files.
The icp.yaml file configures the ICP settings such as error metric and outlier filters. Any filter that is applied to the map can also be defined here.
The input_filters.yaml file configures operations that are applied to each scan of the range sensors. Subsampling, cropping and normal computation are configured in this file. Two examples have been provided (one for the velodyne puck range sensor and the other one for the ouste OS1 sensor).
The filtering and the ICP can be configured by adding your own custom configuration .yaml files. Documentation on how to do that can be found here.