Chalito: An Extensible Library for Filtering-Based State Estimation in Quadruped Robots

2026-07-10

AI Summary

This work addresses the lack of a unified benchmarking framework for state estimation algorithms in quadrupedal robotics, which has hindered fair comparison, reproducibility, and iterative improvement of such methods. To bridge this gap, we present the first open-source, extensible benchmark library for filtering-based state estimation. The framework supports importing diverse quadruped robot models directly from URDF files, integrates multiple filtering algorithms, and operates seamlessly in both MATLAB and Python environments. It enables standardized evaluation on both simulated and real-world datasets, facilitating consistent cross-platform, cross-robot, and cross-algorithm testing. By providing a common foundation for development and assessment, the proposed benchmark significantly enhances algorithm comparability, reproducibility, and overall development efficiency in quadrupedal state estimation research.

Abstract

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.

Problem

• state estimation
• quadruped robots
• filtering algorithms
• benchmarking
• reproducibility

Innovation

• state estimation
• quadruped robots
• filtering algorithms
• benchmarking framework
• extensible library