提出迭代地标匹配方法，提升人形机器人定位速度与鲁棒性。
Fast and Robust Localization for Humanoid Soccer Robot via Iterative Landmark Matching
- 通过迭代匹配优化地标关联，无需粒子滤波逐粒子匹配
- 在RoboCup比赛中实现比aMCL快3倍且更准的定位
- 融合IMU数据增强对噪声和误检的抗干扰能力
精准定位对机器人有效运行至关重要。蒙特卡洛定位（MCL）虽常用于已知地图场景，但因需对每个粒子进行地标匹配而计算开销大。人形机器人还面临运动振动带来的传感器噪声及摄像头视角受限的问题。本文提出一种基于迭代地标匹配（ILM）的快速鲁棒定位方法。通过迭代匹配提升地标关联精度，避免使用MCL进行粒子级匹配。结合离群点剔除的位姿估计增强了对测量噪声和错误检测的鲁棒性。此外，可额外融合惯性测量单元（IMU）的惯性数据与定位位姿。实验对比显示，ILM在初始猜测误差下比ICP更具鲁棒性，且比增强型蒙特卡洛定位（aMCL）更快、更准确。该方法在人形机器人ARTEMIS上于RoboCup 2024成年组足球赛中得到充分验证。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。