# Fast and Robust Localization for Humanoid Soccer Robot via Iterative Landmark Matching

arXiv: https://arxiv.org/abs/2503.11020 · HTML: https://mlsanity.com/paper/2503.11020/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 39 references that are in the map.
- First posted on arXiv 14 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Deep Closest Point: Learning Representations for Point Cloud Registration](https://mlsanity.com/paper/1905.03304/) (2019)
- [PointNetLK: Robust & Efficient Point Cloud Registration using PointNet](https://mlsanity.com/paper/1903.05711/) (2019)
- [A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS](https://mlsanity.com/paper/2304.00501/) (2023)
- [Grown-up NimbRo Robots Winning RoboCup 2017 Humanoid AdultSize Soccer Competitions](https://mlsanity.com/paper/1809.04928/) (2018)

## Built on by (most cited first, 1 of 1)

- [Humanoid Goalkeeper: Learning from Position Conditioned Task-Motion Constraints](https://mlsanity.com/paper/2510.18002/) (2025)

## Abstract (arXiv)

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.