Chalito: An Extensible Library for Filtering-Based State Estimation in Quadruped Robots
arXiv:2607.09968v1 Announce Type: new 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 MATL
Overview
arXiv:2607.09968v1 Announce Type: new 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.
Source
Originally published at arxiv.org.
Related Articles
- Performance Characterization of Frequency-Selective Wireless Power Transfer Toward Scalable Untethered Magnetic Actuation
- Casting Everything to Online API Services? A Survey of Integrating Localized Speech Recognition Models in Robotic Systems
- ReMoSPLAT: Reactive Mobile Manipulation Control on a Gaussian Splat
Source: https://arxiv.org/abs/2607.09968