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.

翻译：精确的机器人定位是实现有效操作的关键。蒙特卡洛定位（MCL）方法在已知地图场景中应用广泛，但由于需要为每个粒子进行地标匹配，其计算开销较大。人形机器人还面临额外挑战，包括由运动振动引起的传感器噪声，以及因摄像头安装位置导致的有限视野（FOV）。本文提出一种基于迭代地标匹配（ILM）的、适用于人形机器人的快速鲁棒定位方法。该迭代匹配过程提升了地标关联的准确性，从而无需依赖MCL将地标与粒子进行匹配。结合异常值剔除过程的位姿估计增强了方法对测量噪声与错误检测的鲁棒性。此外，可采用一个额外的滤波器来融合来自惯性测量单元（IMU）的惯性数据与定位输出的位姿数据。我们将ILM与迭代最近点（ICP）方法进行比较，结果表明ILM方法对初始猜测误差具有更强的鲁棒性，且更容易获得正确匹配。我们还将ILM与增强蒙特卡洛定位（aMCL）方法进行对比，结果显示ILM方法远快于aMCL，且精度更高。通过实验对所提方法的有效性进行了全面评估，并在RoboCup 2024成人组足球比赛中的人形机器人ARTEMIS上进行了验证。