This is the exraction of the localization and mapping packages from the Autoware AI
I am not the original author on this software and the license is in accordance with Autoware AI.
lidar_localizer package
ndt_mapping
- input
/points_raw (sensor_msgs/PointCloud2) - output
/ndt_map (sensor_msgs/PointCloud2)
/curent_pose (geometry_msgs/PoseStamped)
ndt_matching
-
input
/filtered_points (sensor_msgs/PointCloud2)
/points_map (sensor_msgs/PointCloud2)
/initialpose (geometry_msgs/PoseWithCovarianceStamped) -
output
/curent_pose (geometry_msgs/PoseStamped)
ndt
| Name | Type | Description | Default value |
|---|---|---|---|
| max_iter | int | max iteration for alignment | 25 |
| step_size | double | step_size maximum step length[m] | 0.1 |
| ndt_res | double | resolution side length of voxels[m] | 1.0 |
| transform_epsilon | double | transform epsilon to stop iteration | 0.1 |
| voxel_leaf_size | double | a down sample size of a input cloud[m] | 0.2 |
ndt_mapping
| Name | Type | Description | Default value |
|---|---|---|---|
| min_add_scan_shift | double | a moving distance of a map update[m] | 1.5 |
rviz -d src/lidar_localizer/config/mapping.rviz
roslaunch lidar_localizer ndt_mapping.launch
to save a map
rosrun pcl_ros pointcloud_to_pcd input:=/ndt_map prefix:=map
When processing long distance data, it is recommended to use ndt_mapping_submaps instead of ndt_mapping.
rviz -d src/lidar_localizer/config/matching.rviz
roslaunch lidar_localizer ndt_matching.launch
rostopic pub /initialpose geometry_msgs/PoseWithCovarianceStamped '{header:{frame_id: "map"},pose: {pose: {position: {x: 0, y: 0, z: 0}, orientation: {z: 0, w: 1}}}}'
rosrun pcl_ros pcd_to_pointcloud map_0.pcd /cloud_pcd:=/points_map _frame_id:=map