External Odometry integration

Posted by AlmogT on
URL: http://official-rtab-map-forum.206.s1.nabble.com/External-Odometry-integration-tp11047.html

Hi everyone,

I'm working on a handheld SLAM system and would appreciate guidance on how to properly integrate RTAB-Map into my pipeline to generate the most accurate 3D map.

My current setup includes:
Custom Visual-Inertial Odometry (VIO): Based on stereo cameras and IMU. I plan to feed RTAB-Map a post-processed, smooth trajectory (i.e., already loop-closed, no jumps).

LiDAR: A solid-state 3D LiDAR with an 80° horizontal FOV and 10 Hz scan rate, producing relatively dense short-range point clouds (up to ~60m).

IMU: XSens IMU at 100 Hz. It is already used within the VIO, but I can also publish it separately if useful for RTAB-Map.

My goals:
Use RTAB-Map only for 3D map building, not for localization.
Feed the VIO trajectory as external odometry input.
Integrate LiDAR point clouds into the map.
Optionally provide IMU data, if it improves mapping quality.

My questions:
Are there any examples of a similar setup? For example, a launch file that shows how to integrate external odometry with RTAB-Map?

Given the LiDAR's limited 80° FOV, are there recommended parameters or techniques to improve scan matching or overall map quality?

Should I provide IMU data to RTAB-Map even though it’s already used in the VIO? Would this enhance mapping accuracy or risk introducing redundant/noisy data?

Any key RTAB-Map parameters you recommend tuning for this type of hybrid setup? (e.g., memory management, keyframe thresholds, sensor fusion settings)

My stereo cameras are approximately synchronized (within a few milliseconds). How should I feed them into RTAB-Map? Do I need to insert a synchronization node between the image topics?

Below is the RTAB-Map launch configuration I’m currently using:

rtabmap_slam = Node(
    package='rtabmap_slam', executable='rtabmap', output='screen',
    parameters=[{
        'namespace': 'rtabmap',
        'frame_id': 'scaner_base',
        'subscribe_depth': False,
        'subscribe_rgb': False,
        'subscribe_scan_cloud': True,
        'approx_sync': True,
        'wait_for_transform': 0.2,
        'use_sim_time': use_sim_time,
        # RTAB-Map's internal parameters are strings:
        # 'odom_frame_id': 'world',
        'Grid/FromDepth': 'False',
        'Grid/3D': 'True',            # Enable 3D grid mapping
        'Grid/Color': 'True',         # Enable color in the grid mapping
        'Grid/RayTracing': 'False',   # Optional: Enable ray tracing if not using depth
        'Grid/MapFrame': 'map',       # Ensure this matches your TF setup
    }],
    remappings=[
        ('scan_cloud', '/lidar/scan_3D'),
        ('imu', '/imu/data'),
        ('odom', '/state_optimizer/odometry_corrected')
    ],
    arguments=[
        '-d'  # This will delete the previous database (~/.ros/rtabmap.db)
    ])



However, with this setup, I’m currently getting a poor-quality map — the map is sparse, inconsistent, and not well-aligned with the environment. I'm not sure if the issue is related to TF frames, sensors sync, or odometry integration.

Any insights or advice would be greatly appreciated!