反復ランドマークマッチングによるヒューマノイドサッカーロボットの高速で堅牢な自己位置推定
Fast and Robust Localization for Humanoid Soccer Robot via Iterative Landmark Matching
ヒューマノイドロボット向けに、反復的なランドマークマッチングで外れ値除去とIMU融合を行う高速・高精度な自己位置推定手法を提案し、RoboCup 2024で実機検証した。
著者: Ruochen Hou, Mingzhang Zhu, Hyunwoo Nam, Gabriel I. Fernandez, Dennis W. Hong
分類: cs.RO, cs.CV
原文アブストラクト
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.
