Lidar Odometry

name: lidar-odometry description: 'Set up lidar odometry. Use when using laser-based ego-motion with rf2o_laser_odometry or KISS-ICP.'

Lidar Odometry — Scan Matching for Motion Estimation

Overview

Lidar odometry estimates robot motion by matching consecutive laser scans (2D) or point clouds (3D). It provides a nav_msgs/Odometry output that can supplement or replace wheel odometry, especially on surfaces with poor traction.

Available Packages

rf2o_laser_odometry

How It Works

rf2o computes scan-to-scan motion by formulating the problem as a range flow constraint. Instead of feature extraction and matching, it directly estimates the rigid-body transform that minimizes the difference between consecutive scan ranges. This makes it fast and robust in geometrically rich environments.

Configuration

rf2o_laser_odometry_node:
  ros__parameters:
    laser_scan_topic: "/scan"
    odom_topic: "/odom_rf2o"
    base_frame_id: "base_link"        # Robot body frame
    odom_frame_id: "odom_rf2o"        # Output frame (use distinct name if not replacing wheel odom)
    publish_tf: false                  # Let EKF handle TF
    freq: 20.0                         # Processing rate (Hz)
    init_pose_from_topic: ""           # Leave empty for default initialization
    verbose: false

Launch File

from launch import LaunchDescription
from launch_ros.actions import Node

def generate_launch_description():
    return LaunchDescription([
        Node(
            package='rf2o_laser_odometry',
            executable='rf2o_laser_odometry_node',
            name='rf2o_laser_odometry',
            parameters=[{
                'laser_scan_topic': '/scan',
                'odom_topic': '/odom_rf2o',
                'base_frame_id': 'base_link',
                'odom_frame_id': 'odom',
                'publish_tf': False,
                'freq': 20.0,
            }],
            output='screen',
        ),
    ])

kiss-icp (3D Lidar)

For robots with 3D lidar (e.g., Ouster, Velodyne, Livox):

kiss_icp_node:
  ros__parameters:
    topic: "/points"
    base_frame: "base_link"
    odom_frame: "odom"
    publish_odom_tf: false
    max_range: 100.0
    min_range: 0.5
    voxel_size: 0.5           # Downsampling resolution (meters)
    max_points_per_voxel: 20
    initial_threshold: 2.0     # Adaptive ICP threshold start
    min_motion_th: 0.1         # Minimum motion to trigger registration

Strengths and Weaknesses

When Lidar Odometry Excels

• Feature-rich environments: offices with walls, furniture, doors — many geometric features to match against
• Wheel slip scenarios: smooth floors, ramps, thresholds where encoders are unreliable
• Constant lighting: unlike visual odometry, lidar is unaffected by illumination

When Lidar Odometry Struggles

• Long featureless corridors: parallel walls provide only one constraint axis, causing drift along the corridor
• Symmetric environments: identical rooms or hallways cause ambiguity
• Open spaces: sparse features lead to poor scan matching
• Dynamic environments: many moving obstacles corrupt scan matching (people, pets)

EKF Integration

As Supplement to Wheel Odometry

Use differential mode for lidar odom, similar to visual odometry:

ekf_filter_node:
  ros__parameters:
    # Primary: wheel odometry
    odom0: "odom/unfiltered"
    odom0_config: [false, false, false,
                   false, false, false,
                   true,  false, false,
                   false, false, true,
                   false, false, false]

    # Secondary: lidar odometry (differential)
    odom1: "odom_rf2o"
    odom1_config: [true,  true,  false,
                   false, false, true,
                   false, false, false,
                   false, false, false,
                   false, false, false]
    odom1_differential: true
    odom1_queue_size: 5

    imu0: "imu/data"
    imu0_config: [false, false, false,
                  false, false, true,
                  false, false, false,
                  false, false, true,
                  true,  false, false]
    imu0_remove_gravitational_acceleration: true

As Replacement for Wheel Odometry

If wheels are highly unreliable, lidar odom can be the primary source. Fuse velocities directly (non-differential):

odom0: "odom_rf2o"
odom0_config: [false, false, false,
               false, false, false,
               true,  false, false,
               false, false, true,
               false, false, false]
odom0_differential: false

Warning: If both lidar odom and wheel odom provide vyaw, the EKF weights them by covariance. Ensure covariances are realistic — don't set both to near-zero.

scan_matcher_karto (Alternative)

Part of the SLAM Toolbox ecosystem, scan_matcher_karto uses correlation-based scan matching:

scan_matcher_karto_node:
  ros__parameters:
    use_scan_matching: true
    use_scan_barycenter: true
    resolution: 0.05
    range_threshold: 12.0
    minimum_travel_distance: 0.1
    minimum_travel_heading: 0.1

This is heavier than rf2o but can be more robust in environments with sparse features.

Performance Comparison

Troubleshooting