AMCL (Adaptive Monte Carlo Localisation)

What is AMCL (Adaptive Monte Carlo Localisation)?

AMCL (Adaptive Monte Carlo Localisation) is a particle-filter algorithm that localises a robot in a previously built map using LIDAR (or sonar) data. It is the default localiser in ROS2 Nav2 and runs on thousands of mobile robots worldwide.

How It Works

AMCL maintains a population of particle hypotheses about the robot's pose. At each control step it predicts each particle forward using odometry plus noise, then weights it by how well its predicted scan matches the actual LIDAR scan. Resampling concentrates particles on high-likelihood poses. The 'adaptive' part dynamically adjusts the number of particles based on convergence — fewer when localised, more when uncertain (e.g., after the robot is kidnapped).

Real-World Example

Almost every Nav2-based robot uses AMCL out of the box. GreyOrange Butlers, Robotis TurtleBots, hospital delivery robots, and Indian academic mobile-robotics projects all rely on it. AMCL handles minor map changes (people walking by) gracefully.

Why It Matters for Robotics

AMCL is the first localiser most ROS2 users touch. Knowing how to tune its parameters — number of particles, motion model noise, laser model — is essential for any real deployment. AMCL bugs (poor convergence, slow recovery) are a daily occurrence in production.

Try It Yourself

Launch the Nav2 TurtleBot3 demo in Gazebo. Use RViz to publish an initial pose estimate and watch the particle cloud collapse. Then teleport the robot to a random spot and use the '2D Pose Estimate' tool to recover — feel AMCL converge.

Quick Quiz

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Quick Quiz
3 questions
1.AMCL is based on which underlying algorithm?
A.Kalman filter
B.Particle filter
C.A* search
D.Gradient descent
2.AMCL requires:
A.Only GPS
B.A pre-built map plus laser/sensor data
C.No sensors at all
D.A neural network
3.The "adaptive" in AMCL refers to:
A.Adaptive Wi-Fi
B.Adjusting the number of particles based on convergence
C.Changing the robot colour
D.Adapting the battery

Further Reading

• Particle Filter
• Slam
• Lidar Slam
• Occupancy Grid
• Nav2 Stack

Ask R2 About This

Open the R2 Co-pilot (press ⌘K anywhere on R2BOT) and ask: "Explain AMCL (Adaptive Monte Carlo Localisation) for a Class 9 student in India, with one real-world Indian example." You'll get a tailored, sourced answer in seconds.