amcl-tuning

What problem does it solve?

Tune AMCL particle filter localization by adjusting motion, laser, and recovery parameters to improve pose estimation accuracy and robustness in dynamic environments.

Core Features & Use Cases

• Motion model tuning: adjust alpha1–alpha5 to match odometry noise and improve tracking during turns and straight-line motion.
• Laser model tuning: select the likelihood field or beam model and tune sigma_hit, laser_likelihood_max_dist, and max_beams for a balance of accuracy and performance.
• Recovery & update controls: configure recovery_alpha_fast/slow and update_min_d to enable re-localization after kidnap and reduce unnecessary updates in noisy data.
• Real-world use case: In a cluttered indoor environment, tune parameters to maintain a stable map pose during occlusions and dynamic obstacles.

Quick Start

Run this guide to adjust alpha parameters, laser settings, and update thresholds on your AMCL configuration and observe localization improvements.