Localization Operations

This guide covers robot localization procedures for the RCR Common Robotics Platform.

Overview

Localization is the process of determining the robot’s position and orientation within a known map. The system uses AMCL (Adaptive Monte Carlo Localization) to track the robot’s pose.

Prerequisites

  • Map of the environment available

  • LiDAR sensor functioning

  • IMU and odometry data available

  • Navigation stack configured

Localization Setup

1. Launch Localization

# Launch localization system
ros2 launch common_platform localization_launch.py

2. Verify Topics

# Check required topics
ros2 topic list | grep -E "(scan|odom|map|amcl)"

Expected topics:

  • /scan - LiDAR data

  • /odom - Odometry data

  • /map - Map data

  • /amcl_pose - Localized pose

Initial Localization

1. Set Initial Pose

Using RViz:

  1. Open RViz

  2. Click “2D Pose Estimate”

  3. Click and drag on map to set initial pose

Using Command Line:

# Set initial pose
ros2 topic pub /initialpose geometry_msgs/msg/PoseWithCovarianceStamped "{}"

2. Verify Localization

# Monitor pose estimate
ros2 topic echo /${ROS_NAME}/amcl_pose

# Check pose covariance
ros2 topic echo /${ROS_NAME}/amcl_pose | grep covariance

Localization Parameters

AMCL Configuration

Key parameters in nav2_params.yaml:

amcl:
  ros__parameters:
    # Particle filter parameters
    min_particles: 500
    max_particles: 2000

    # Update parameters
    update_min_d: 0.2
    update_min_a: 0.5

    # Laser model parameters
    laser_max_range: 10.0
    laser_min_range: 0.1

Tuning Guidelines

For Better Accuracy:

  • Increase particle count

  • Decrease update thresholds

  • Tune laser model parameters

For Better Performance:

  • Decrease particle count

  • Increase update thresholds

  • Optimize laser parameters

Localization Monitoring

1. Pose Quality

Check Covariance:

# Monitor pose covariance
ros2 topic echo /amcl_pose | grep covariance

Low covariance values indicate good localization.

2. Particle Cloud

Visualize in RViz:

  1. Add “PoseArray” display

  2. Set topic to /particlecloud

  3. Monitor particle distribution

3. Localization Status

# Check localization status
ros2 topic echo /${ROS_NAME}/localization_status

Troubleshooting

Common Issues

Poor Localization:

  • Check map quality

  • Verify sensor data

  • Tune AMCL parameters

  • Check for dynamic obstacles

Localization Failures:

  • Verify initial pose

  • Check sensor connections

  • Review error logs

  • Test in known location

Drift Issues:

  • Check odometry accuracy

  • Verify IMU calibration

  • Review sensor fusion

  • Test on different surfaces

Diagnostic Commands

# Check sensor data quality
ros2 topic hz /${ROS_NAME}/scan
ros2 topic hz /${ROS_NAME}/odom

# Monitor localization performance
ros2 topic echo /${ROS_NAME}/amcl_pose

# Check for errors
ros2 log list

Best Practices

Environment Setup

  • Use high-quality maps

  • Ensure good LiDAR coverage

  • Minimize dynamic obstacles

  • Maintain consistent lighting

Operation

  • Set accurate initial pose

  • Monitor localization quality

  • Avoid areas with poor features

  • Use landmarks for verification

Maintenance

  • Regular sensor cleaning

  • Periodic recalibration

  • Map updates as needed

  • Performance monitoring

Advanced Localization

Multi-Hypothesis Tracking

  • Handle ambiguous situations

  • Maintain multiple hypotheses

  • Use additional sensors

Sensor Fusion

  • Combine multiple sensors

  • Use IMU for orientation

  • Integrate visual features

Dynamic Environments

  • Handle moving obstacles

  • Update maps in real-time

  • Use adaptive algorithms

For mapping procedures, see Mapping For path planning, see Path Planning For troubleshooting, see Troubleshooting