开源工具库Chalito统一评测四足机器人滤波状态估计算法
Chalito: An Extensible Library for Filtering-Based State Estimation in Quadruped Robots
- 基于URDF模型导入，支持多种滤波方法扩展
- 可在仿真与真实数据上跨机器人系统评估算法性能
- 首个专注四足机器人滤波算法的开源基准框架
状态估计对四足机器人的稳定行走、导航与控制至关重要。尽管已有诸多估计算法被提出，但现有实现通常绑定特定机器人或软件栈，导致公平比较困难。缺乏通用基准框架阻碍了结果复现与算法创新。本文提出Chalito，一个可扩展的MATLAB/Python库，用于在四足机器人上基准测试基于滤波的状态估计算法。该框架可直接从URDF导入机器人模型，支持多种滤波方法，并可轻松扩展新算法。其运行于仿真与真实数据集，实现跨机器人与滤波器的系统性评估。据我们所知，这是首个专用于四足机器人滤波算法基准测试的开源库。
原文摘要 · Abstract (English)
State estimation is essential for quadruped robots, enabling robust locomotion, navigation, and control. While many estimators have been proposed in the literature, existing implementations are often tied to specific robots or software stacks, making fair comparisons difficult. This lack of a general-purpose benchmarking framework hinders reproducibility and slows down algorithmic innovation. In this paper, we introduce Chalito, an extensible MATLAB/Python library for benchmarking filter-based state estimation algorithms in quadruped robots. Chalito imports robot models directly from URDF, supports multiple filtering approaches, and is designed to be easily extended with new methods. The framework runs on both simulated and real datasets, enabling systematic evaluation across robots and filters. To the best of our knowledge, this is the first open-source library exclusively dedicated to benchmarking filtering algorithms for quadruped robots.
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