Fast and Robust Localization for Humanoid Soccer Robot via Iterative Landmark Matching
This work addresses localization challenges for humanoid robots in dynamic environments like RoboCup soccer, representing an incremental improvement over prior methods.
The paper tackles the problem of accurate and efficient localization for humanoid soccer robots by proposing an iterative landmark matching (ILM) method, which is shown to be faster and more accurate than existing approaches like Augmented Monte Carlo Localization (aMCL) and more robust to initial errors than Iterative Closest Point (ICP).
Accurate robot localization is essential for effective operation. Monte Carlo Localization (MCL) is commonly used with known maps but is computationally expensive due to landmark matching for each particle. Humanoid robots face additional challenges, including sensor noise from locomotion vibrations and a limited field of view (FOV) due to camera placement. This paper proposes a fast and robust localization method via iterative landmark matching (ILM) for humanoid robots. The iterative matching process improves the accuracy of the landmark association so that it does not need MCL to match landmarks to particles. Pose estimation with the outlier removal process enhances its robustness to measurement noise and faulty detections. Furthermore, an additional filter can be utilized to fuse inertial data from the inertial measurement unit (IMU) and pose data from localization. We compared ILM with Iterative Closest Point (ICP), which shows that ILM method is more robust towards the error in the initial guess and easier to get a correct matching. We also compared ILM with the Augmented Monte Carlo Localization (aMCL), which shows that ILM method is much faster than aMCL and even more accurate. The proposed method's effectiveness is thoroughly evaluated through experiments and validated on the humanoid robot ARTEMIS during RoboCup 2024 adult-sized soccer competition.