Multi-session use case: CLA, multi-floor use-case

This tutorial will show you how to reproduce the large scale multi-session mapping use-case presented in the paper.

Datasets:

You can download the rosbags and camera/IMU calibration here.

The files you need:

• bags

  • cla_floor_f.bag

  • cla_floor_g.bag

  • cla_floor_h.bag

  • cla_floor_j.bag

  • cla_floor_f_v2_for_localization.bag

    • Intended to be used to test the localization, not to build the localization

• calibration

  • imu-adis16488_cla.yaml

  • imu-sigmas-rovio_cla.yaml

  • imu-adis16488_cla.yaml

Alternatively, you can download the prebuilt maps directly from our dataset page.

Instructions:

1. Use the [[Running ROVIOLI in VIO mode]] tutorial to create the initial open-loop maps.

   Important: Use the camera/imu calibration provided with the dataset.

    --ncamera_calibration=ncamera_cla.yaml
    --imu_parameters_maplab=imu-adis16488_cla.yaml 
    --imu_parameters_rovio=imu-sigmas-rovio_cla.yaml
   

   In order to create the maps, make sure you use these flags:

   # We don't need images for this, they will only lead to a large map folder.
   --map_builder_save_image_as_resources=false
   # This is not strictly needed, but if set to true, this will create a
   # localization map version for each map, which depending on the current 
   # implementation of ROVIOLI might include key-framing or summarization,
   # which we would like to avoid for now.
   --optimize_map_to_localization_map=false
   

   You might want to use these flags as well:

   # Visualize the ROVIO vio results.
   --rovio_enable_frame_visualization=true
   # Increase playback speed 2x.
   --vio_rosbag_realtime_playback_rate=2.0
   # Will overwrite the vi-map if there is already one 
   # stored in the target folder.
   --overwrite=true
   # Increase verbosity of ROVIOLI
   --v=1
   

2. Check your maps.

   After step 1 you should have the following maps/map structure (with whatever names you chose):

   <your_dataset_folder_path>
   ├── cla_f_vi_map
   │   ├── metadata
   │   ├── resource_info
   │   ├── resources
   │   └── vi_map
   │       ├── edges
   │       ├── landmark_index
   │       ├── missions
   │       ├── optional_sensor_data
   │       ├── sensors.yaml
   │       ├── vertices0
   │       ├── vertices1
   │       ├── ...
   │       └── vertices17
   ├── cla_g_vi_map
   │   ├── metadata
   │   ├── resource_info
   │   ├── resources
   │   └── vi_map
   │       ├── edges
   │       ├── landmark_index
   │       ├── missions
   │       ├── optional_sensor_data
   │       ├── sensors.yaml
   │       ├── vertices0
   │       ├── ...
   │       └── vertices22
   ├── cla_h_vi_map
   │   ├── metadata
   │   ├── resource_info
   │   ├── resources
   │   └── vi_map
   │       ├── edges
   │       ├── landmark_index
   │       ├── missions
   │       ├── optional_sensor_data
   │       ├── sensors.yaml
   │       ├── vertices0
   │       ├── ...
   │       └── vertices24
   └── cla_j_vi_map
       ├── metadata
       ├── resource_info
       ├── resources
       └── vi_map
           ├── edges
           ├── landmark_index
           ├── missions
           ├── optional_sensor_data
           ├── sensors.yaml
           ├── vertices0
           ├── ...        
           └── vertices28
   

3. Align, loop-close and optimize maps:

   Load all maps, join, align, relax, loop close and optimize them.

   load --map_folder=<your_dataset_folder_path>/cla_f_vi_map
   load --map_folder=<your_dataset_folder_path>/cla_g_vi_map
   load --map_folder=<your_dataset_folder_path>/cla_h_vi_map
   load --map_folder=<your_dataset_folder_path>/cla_j_vi_map
   # This will create a joint map with 4 missions
   join_all_maps --target_map_key cla_f_g_h_j_vi_map
   # Visualize all missions with different colors.
   v --vis_color_by_mission
   

   You should see the following:

   # Set one of the base frames to "known" to be able 
   # to anchor all other maps with respect to that.
   sbk
   # Align all missions.
   aam
   # Visualize all missions with different colors.
   v --vis_color_by_mission
   

   Your map should now look like this:

   The next step is optional, but will speed up relax, the loop closure and bundle adjustment significantly.

   # Key-frame the map.
   kfh
   

   Pose-graph relaxation to close large loops:

   relax
   

   This should have closed the large loops and aligned the floors:

   # Loop close the map
   lc
   # Bundle adjustment
   optvi
   # You can further increase the quality of the map by repeating
   # lc + optvi or using --ba_num_iterations=50
   

   Done: