发表机构
Pontifical Catholic University of Rio de Janeiro; Istituto Italiano di Tecnologia（里约热内卢天主教大学; 意大利技术研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对四足机器人状态估计，提出Chalito库，它可从URDF导入模型，支持多种滤波方法，能在模拟和真实数据集上运行，实现对机器人和滤波器的系统评估，是首个专用于四足机器人滤波算法基准测试的开源库。
AI 中文摘要
状态估计对四足机器人至关重要，能实现稳健运动、导航和控制。虽文献中已提出许多估计器，但现有实现常与特定机器人或软件栈绑定，难以进行公平比较，缺乏通用基准框架阻碍了可重复性并减缓算法创新。本文介绍了Chalito，一个用于对四足机器人基于滤波的状态估计算法进行基准测试的可扩展MATLAB/Python库。它直接从URDF导入机器人模型，支持多种滤波方法，易于用新方法扩展。该框架可在模拟和真实数据集上运行，实现对机器人和滤波器的系统评估。据我们所知，这是首个专门用于四足机器人滤波算法基准测试的开源库。
英文摘要
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.
