This is an offline version of LIO-SAM that produces a determenstic output, through exchanging messages/data using pointers instead of topics. It is configured to run with ouster lidar packets and pointclouds. This mapper can output different pointclouds to PCD files as well as a CSV file containing the poses at each keyframe.
Todo.
- Download and unzip court_yard_stroll_filtered.zip.
- Modify some parameters in param.yaml.
- Set
readBagto the path of court_yard_stroll_filtered.zip. - Set
SaveDireto a directory where the output files could be saved. - Set
pointCloudTopicto/os_cloud_node/points.
- Set
- Run
roslaunch lio_sam run.launch
Below are the additional parameters added, information about the other parameters might be found in LIO-SAM.
| Parameter | Type | Description |
|---|---|---|
| readBag | String | Rosbag path |
| saveDir | String | Saving directory |
| saveToRosbag | Bool | Set to True to save global map and trajectory to a rosbag |
| saveTrajectoryCSV | Bool | Set to True to save trajectory to CSV |
| saveRawPCD | Bool | Set to True to save raw lidar pointclouds to PCDc (lidar frame) |
| saveDeskewedPCD | Bool | Set to True to save deskewed pointclouds to PCDs (lidar frame) |
| saveRegisteredCloudPCD | Bool | Set to True to save registered pointclouds to PCDs (map frame) |
| saveRegisteredFeaturesPCD | Bool | Set to True to save registered feature pointclouds to PCDs (map frame) |
| LMOMaxIterations | Int | Max iterations count for lidar odometry optimisation |