Launching MOLA-LO

1. MOLA-LO GUI applications

All these commands are actually shortcuts (small scripts) to launch mola-cli and one of the predefined mola-cli launch files containing one dataset source and the mola_viz module, which enables the real-time GUI visualization.

First, we enumerate the existing scripts together with their specific environment variables and CLI usage, next we list the environment variables common to all of them.

Note

Apart of the environment variables defined here, which are specific to these mola-cli launch files (see: role of MOLA-LO module), remember that there are many additional parameters associated to the LO pipeline YAML file, listed here.

Common elements of all GUI apps

mola_viz provides a unified API for all MOLA modules to display custom UI subwindows, controls, and 3D objects. Next we describe how to manipulate or interpret the common elements in this UI, which is the same in all MOLA-LO programs, except mola-lidar-odometry-cli which is explicitly design not to have any GUI.

The labeled parts in the GUI are:

The main space of the “main” GUI window: used to render the latest 3D local map, together with the incoming raw scan, the 3D vehicle model (if provided), etc. Use the same mouse and keyboard shortcuts than in any MRPT 3D window to rotate the view, pan and zoom, etc. The rest of small windows floating in this large windows are dubbed “subwindows”.
Dataset source UI: All dataset sources (offline datasets, rawlog, rosbag2, etc.) offer a common API that can be operated through these controls to pause or resume the publication of observations to the LiDAR front-end or to change the playback speed. Most datasets also support directly jumping forth and back in time by clicking on the time slider (It is recommended to pause mapping and reset the map while doing so!).
Main subwindows control panel: You can minimize, restore, etc. subwindows from here.
Raw LiDAR view: A subwindow with the details (type of observation, timestamp, etc.) of incoming raw scans.
Camera view: Although not used by the LO, it is useful sometimes to see the environment from camera images.
mola_lidar_odometry module own UI: Direct access to the internals of the LO module. There are tabs providing direct access to variables that normally can be set via environment variables, to make it easier to record a simple-maps directly from the GUI.
Log messages with priority higher than INFO will be dumped to both, the terminal, and to this scrolling transparent terminal at the bottom.

1.1. mola-lo-gui-kitti

Runs MOLA-LO on a sequence of the KITTI odometry dataset [GLSU13].

1.2. mola-lo-gui-kitti360

Runs MOLA-LO on a sequence of the KITTI-360 dataset.

1.3. mola-lo-gui-mulran

Runs MOLA-LO on a sequence of the Mulran dataset.

1.4. mola-lo-gui-rawlog

This command will open the mola_viz GUI and build a map from dataset stored as a .rawlog file.

Environment variables specific for mola-lo-gui-rawlog:

MOLA_TIME_WARP (Default: 1.0): Time wrapping for rosbag replay.
MOLA_DATASET_START_PAUSED (Default: false): Start with replay paused. Then can be resumed from the GUI.
MOLA_INPUT_RAWLOG_READ_ALL_FIRST (Default: false): Set to true to load the whole .rawlog file at start up, enabling the use of the dataset timeline slider in the UI.

1.5. mola-lo-gui-rosbag2

This command will open the mola_viz GUI and build a map from ROS 2 bags:

# Usage for a single bag file:
MOLA_LIDAR_TOPIC=/ouster/points \
mola-lo-gui-rosbag2 /path/to/your/dataset.mcap

# Usage for a directory with split bag files:
# Note: in this case, the directory is expected to contain a metadata.yaml to determine "rosbag_storage_id".
MOLA_LIDAR_TOPIC=/ouster/points \
mola-lo-gui-rosbag2 /path/to/your/dataset_directory/

Environment variables specific for mola-lo-gui-rosbag2:

See complete mola launch YAML listing

File: mola-cli-launchs/lidar_odometry_from_rosbag2.yaml

# -----------------------------------------------------------------------------
#                        SLAM system definition for MOLA
# This file defines:
# An input sensor from a ROS 2 bag file, and the MOLA Visualizer.
# -----------------------------------------------------------------------------

modules:
  # =====================
  # MolaViz
  # =====================
  - name: viz
    type: mola::MolaViz
    #verbosity_level: DEBUG
    params: ~ # none

  # =====================
  # LIDAR ODOMETRY
  # =====================
  - name: icp_odom
    type: mola::LidarOdometry
    #verbosity_level: DEBUG
    raw_data_source: 'dataset_input'
    # This includes here a whole block "params: (...)"
    # with the LIDAR-Odometry pipeline configuration:
    params: '${MOLA_ODOMETRY_PIPELINE_YAML|../pipelines/lidar3d-default.yaml}'

  # =========================
  # Offline dataset source
  # =========================
  - type: mola::Rosbag2Dataset
    name: dataset_input
    # In Rosbag2Dataset, this execution rate (Hz) determines the
    # maximum rate of publishing topics from the bag file.
    # If a message in the bag has an actually rate higher than this, their
    # messages will be still sent out with the correct timestamps, but physically
    # in bursts.
    execution_rate: 50 # Hz
    #verbosity_level: INFO
    quit_mola_app_on_dataset_end: ${MOLA_QUIT_ON_DATASET_END|false}

    # Optional visualization of raw sensor streams in the MolaViz GUI
    gui_preview_sensors:
      - raw_sensor_label: '${MOLA_LIDAR_NAME|lidar}'
        decimation: 1
        win_pos: 5 5 400 400
        
      #- raw_sensor_label: '/ouster/range_image'
      #  decimation: 1
      #  win_pos: 5 420 400 400

    params:
      # We use an environment variable to force the user to specify a bag file:
      rosbag_filename: ${MOLA_INPUT_ROSBAG2}
      time_warp_scale: ${MOLA_TIME_WARP|1.0}
      start_paused: ${MOLA_DATASET_START_PAUSED|false}
      base_link_frame_id: '${MOLA_TF_BASE_FOOTPRINT|base_link}'

      # 'sensors' contains maps from output "sensorLabel"s, to the
      # configuration of that data source (mainly, 'type', 'topic'):
      # If no 'sensors' entry is given, all ROSBAG topics will be exposed
      # to the MOLA subsystem. Note that if you want to override sensor poses
      # (or if you do not have /tf), it is mandatory to define 'sensors' and
      # define 'fixed_sensor_pose' for each sensor here:
      sensors:
        - topic: ${MOLA_LIDAR_TOPIC|'/ouster/points'}
          type: CObservationPointCloud
          sensorLabel: '${MOLA_LIDAR_NAME|lidar}'
          # If present, this will override whatever /tf tells about the sensor pose:
          fixed_sensor_pose: "${LIDAR_POSE_X|0} ${LIDAR_POSE_Y|0} ${LIDAR_POSE_Z|0} ${LIDAR_POSE_YAW|0} ${LIDAR_POSE_PITCH|0} ${LIDAR_POSE_ROLL|0}"  # 'x y z yaw_deg pitch_deg roll_deg'
          use_fixed_sensor_pose: ${MOLA_USE_FIXED_LIDAR_POSE|false}

        - topic: ${MOLA_GNSS_TOPIC|'/gps'}
          sensorLabel: 'gps'
          type: CObservationGPS
          fixed_sensor_pose: "${GPS_POSE_X|0} ${GPS_POSE_Y|0} ${GPS_POSE_Z|0} ${GPS_POSE_YAW|0} ${GPS_POSE_PITCH|0} ${GPS_POSE_ROLL|0}"  # 'x y z yaw_deg pitch_deg roll_deg'
          use_fixed_sensor_pose: ${MOLA_USE_FIXED_GNSS_POSE|false}

        #- topic: /ouster/range_image
        #  type: CObservationImage
        #  fixed_sensor_pose: "0 0 0 0 0 0"  # 'x y z yaw_deg pitch_deg roll_deg'

        #- topic: ${MOLA_ODOMETRY_TOPIC|'/odom'}
        #  type: CObservationOdometry

MOLA_TIME_WARP (Default: 1.0): Time wrapping for rosbag replay.
MOLA_DATASET_START_PAUSED (Default: false): Start with replay paused. Then can be resumed from the GUI.
MOLA_TF_BASE_FOOTPRINT (Default: base_link): Set to something else if your /tf tree does not have a base_link frame.

1.6. Common env variables

PIPELINE_YAML (Default: full path to installed lidar3d-default.yaml): Can be set to override the default pipeline and experiment with custom MOLA-LO systems described through a modified YAML file.

2. LiDAR odometry CLI

mola-lidar-odometry-cli is a standalone command line program to run MOLA-LO on a dataset in an offline fashion. The dataset is processed as fast as possible using all available CPU cores. Its outputs include the vehicle trajectory (as a file in TUM format) and the simple-map, which can be analyzed with sm-cli and used to generate metric maps using sm2mm.

Hint

We recommend using evo to visualize and compare the output TUM trajectories. You can also use mrpt::poses::CPose3DInterpolator to load and parse TUM files in C++, or its Python wrapped version within pymrpt.

2.1. Usage examples

Process a ROS 2 bag

mola-lidar-odometry-cli \
  -c $(ros2 pkg prefix mola_lidar_odometry)/share/mola_lidar_odometry/pipelines/lidar3d-default.yaml \
  --input-rosbag2 /PATH/TO/YOUR/rosbag.mcap \
  --lidar-sensor-label /ouster/points \
  --output-tum-path trajectory.tum \
  --output-simplemap myMap.simplemap

Note

Remember changing --lidar-sensor-label /ouster/points to your actual raw (unfiltered) LiDAR topic (sensor_msgs/PointCloud2).

Process a KITTI dataset sequence

First, make sure of downloading and extracting the dataset files following the layout expected by mola::KittiDataset. Then, set the KITTI_BASE_DIR environment variable and launch the desired sequence (e.g. 00) with:

export KITTI_BASE_DIR=/path/to/kitti_root

mola-lidar-odometry-cli \
  -c $(ros2 pkg prefix mola_lidar_odometry)/share/mola_lidar_odometry/pipelines/lidar3d-default.yaml \
  --input-kitti-seq 00 \
  --output-tum-path kitti-00.tum

2.2. Complete list of arguments

USAGE:

mola-lidar-odometry-cli  [--input-paris-luco] [--input-mulran-seq
                            <KAIST01>] [--input-kitti360-seq <00>]
                            [--kitti-correction-angle-deg <0.205
                            [degrees]>] [--input-kitti-seq <00>]
                            [--input-rosbag2 <dataset.mcap>]
                            [--input-rawlog <dataset.rawlog>]
                            [--lidar-sensor-label <lidar1>] [--skip-first-n
                            <Number of dataset entries to skip>]
                            [--only-first-n <Number of dataset entries to
                            run>] [--output-simplemap
                            <output-map.simplemap>] [--output-tum-path
                            <output-trajectory.txt>] [-l <foobar.so>] [-v
                            <INFO>] -c <demo.yml> [--] [--version] [-h]


Where:

--input-paris-luco
    INPUT DATASET: Use Paris Luco dataset (unique sequence=00)

--input-mulran-seq <KAIST01>
    INPUT DATASET: Use Mulran dataset sequence KAIST01|KAIST01|...

--input-kitti360-seq <00>
    INPUT DATASET: Use KITTI360 dataset sequence number 00|01|...|test_00
    |...

--kitti-correction-angle-deg <0.205 [degrees]>
    Correction vertical angle offset (see Deschaud,2018)

--input-kitti-seq <00>
    INPUT DATASET: Use KITTI dataset sequence number 00|01|...

--input-rosbag2 <dataset.mcap>
    INPUT DATASET: rosbag2. Input dataset in rosbag2 format (*.mcap)

--input-rawlog <dataset.rawlog>
    INPUT DATASET: rawlog. Input dataset in rawlog format (*.rawlog)

--lidar-sensor-label <lidar1>
    If provided, this supersedes the values in the 'lidar_sensor_labels'
    entry of the odometry pipeline, defining the sensorLabel/topic name to
    read LIDAR data from. It can be a regular expression (std::regex)

--skip-first-n <Number of dataset entries to skip>
    Skip the first N dataset entries (0=default, not used)

--only-first-n <Number of dataset entries to run>
    Run for the first N steps only (0=default, not used)

--output-simplemap <output-map.simplemap>
    Enables building and saving the simplemap for the mapping session

--output-tum-path <output-trajectory.txt>
    Save the estimated path as a TXT file using the TUM file format (see
    evo docs)

-l <foobar.so>,  --load-plugins <foobar.so>
    One or more (comma separated) *.so files to load as plugins

-v <INFO>,  --verbosity <INFO>
    Verbosity level: ERROR|WARN|INFO|DEBUG (Default: INFO)

-c <demo.yml>,  --config <demo.yml>
    (required)  Input YAML config file (required) (*.yml)

--,  --ignore_rest
    Ignores the rest of the labeled arguments following this flag.

--version
    Displays version information and exits.

-h,  --help
    Displays usage information and exits.

3. ROS 2 node

A ROS 2 node is provided for running MOLA-LO live on a robot or vehicle where sensor drivers are already working and publishing ROS topics.

3.1. ROS 2 launch file: LiDAR odometry for KITTI

This demo launch file (view sources) runs MOLA-LO on a sequence of the KITTI odometry dataset [GLSU13], demonstrating a few features:

Launching and visualizing LO in both, mola_viz and RViz2 (or use FoxGlove if preferred).
How MOLA core modules and mola_lidar_odometry publish raw observations from a dataset, the local map, the estimated trajectory, and /tf for the vehicle and all sensors.

https://mrpt.github.io/imgs/mola-lo-ros2-launch-demo-kitti.png

3.2. ROS 2 launch file: live LiDAR odometry

This launch file (view sources) runs MOLA-LO live on point clouds received from a ROS 2 topic, demonstrating a few features:

Launching and visualizing LO in both, mola_viz and RViz2 (or use FoxGlove if preferred).
How MOLA ``mola_lidar_odometry``publishes the local map, the estimated trajectory, and /tf for the estimated odometry.

https://mrpt.github.io/imgs/mola-lo-ros2-launch-demo-live-forest.png

4. Running from ROS 1 bags

ROS 1 bags are not directly supported by MOLA-LO. However, given the large amount of public datasets already published in this format, we provide two pathways to parse them.

4.1. Porting to ROS 2 bags

Write me!

4.2. Converting to MRPT rawlog

Write me!