[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-86457-en":3,"doc-seo-86457-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},86457,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Chalito: An Extensible Library for Filtering-Based State Estimation in Quadruped Robots","State estimation is fundamental for quadruped robots to support robust locomotion, navigation, and control through accurate pose, velocity, and contact-state inference. Existing estimator implementations are commonly bound to specific robots, sensor suites, or software stacks, limiting fair comparisons, reproducibility, and educational or prototyping accessibility. Chalito provides an extensible MATLAB/Python benchmarking library that imports robot models from URDF, supports multiple filter families, and runs on simulated and real datasets. Its design is filter-agnostic, easy to extend, and includes PyChalito as a Python-focused alternative tied to MuJoCo workflows.","Chalito: An Extensible Library for Filtering-Based State Estimation  \nin Quadruped Robots  \nHilton Marques Souza Santana 1 , Joo Carlos Virgolino Soares2 , Marco Antonio Meggiolaro 1 and  \nClaudio Semini2  \narXiv :2607 .09968v1 [ cs .RO] 10 Jul 2026  \nAbstract—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 filterbased 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.  \nI. INTRODUCTION  \nState estimation is critical for quadruped robots, as it is an essential step toward achieving robust control and enabling autonomous navigation [1] . Accurate estimation of the robot’s pose, velocity, and contact states is fundamental for locomotion over unstructured or slippery terrain, interaction with the environment, and integration with higher-level planning and control.  \nTo date, several classes of state-estimation filters have been applied to quadruped robots, and an increasing number of publicly available datasets have been released for different robotic platforms and operating scenarios. The most commonly adopted approaches include the Extended Kalman Filter (EKF) [2], [3], the Observability-Constrained EKF [4], the Unscented Kalman Filter [5], and the Invariant EKF [1],[6] . In parallel, the growing availability of public datasets has enabled the evaluation of these methods across a variety of quadruped robots and environments. We show in Table I some sequences of public datasets known to the authors. Despite the progress achieved by these filtering approaches, there is currently no open-source framework that enables a systematic comparison of filter performance in terms of both accuracy and consistency. Likewise, there is no common benchmarking platform that allows researchers to easily evaluate and compare stateestimation algorithms across multiple quadruped robots and  \nThis work was partially financed by the Brazilian Federal Agency for Support and Evaluation of Graduate Education (CAPES), the Carlos Chagas Filho Foundation for Research Support of the State of Rio de Janeiro (FAPERJ) .  \n1H. M. S. Santana and M. A. Meggiolaro are with the Department of Mechanical Engineering at the Pontifical Catholic University of Rio de Janeiro, [hiltonmarquess@gmail.com](hiltonmarquess@gmail.com / meggi@puc-rio.br)[ /](hiltonmarquess@gmail.com / meggi@puc-rio.br)[ meggi@puc-rio.br](hiltonmarquess@gmail.com / meggi@puc-rio.br)  \n[2](2 J. C. V)[ J. C. V](2 J. C. V). Soares and C. Semini are with the Dynamic Legged Systems Lab, Istituto Italiano di Tecnologia, joao .virgolino@iit .it / claudio .semini@iit .it  \nFig. 1: MATLAB implementation of the Chalito architecture.  \ndatasets. Existing implementations are often tightly coupled to specific robots, sensor suites, or software stacks (typically C++/ROS) [7], making it difficult to conduct fair, systematic comparisons across algorithms, platforms, and datasets. This gap hinders reproducibility, slows down the evaluation of new algorithms, and limits accessibility for education and rapid prototyping.  \nIn this work, we aim to address this gap by presenting a MATLAB/Python library for quadruped state estimation that is both URDF 1-driven and filter-agnostic. Our framew","cbCaigSbPkTxO4Po","https://ap.wps.com/l/cbCaigSbPkTxO4Po","pdf",2345093,4,1,9,"English","en",105,"# Introduction\n## Problem: lack of general benchmarking frameworks\n## Proposed solution: Chalito and PyChalito\n# Contributions and evaluations","[{\"question\":\"What problem does Chalito address in quadruped state estimation research?\",\"answer\":\"Chalito addresses the lack of an open, general-purpose benchmarking framework that enables systematic and fair comparisons across filtering algorithms, robots, and datasets.\"},{\"question\":\"What capabilities does Chalito provide for benchmarking filter-based state estimation?\",\"answer\":\"Chalito imports robot models from URDF, supports multiple filtering approaches, runs on both simulated and real datasets, and is designed to be easily extended with new methods.\"},{\"question\":\"How does PyChalito differ from Chalito?\",\"answer\":\"PyChalito is a Python implementation with a different focus: it is tied to work using MuJoCo alongside QuadrupedPyMPC, while Chalito is described as reading a fixed 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problem does Chalito address in quadruped state estimation research?","Question",{"text":75,"@type":76},"Chalito addresses the lack of an open, general-purpose benchmarking framework that enables systematic and fair comparisons across filtering algorithms, robots, and datasets.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What capabilities does Chalito provide for benchmarking filter-based state estimation?",{"text":80,"@type":76},"Chalito imports robot models from URDF, supports multiple filtering approaches, runs on both simulated and real datasets, and is designed to be easily extended with new methods.",{"name":82,"@type":73,"acceptedAnswer":83},"How does PyChalito differ from Chalito?",{"text":84,"@type":76},"PyChalito is a Python implementation with a different focus: it is tied to work using MuJoCo alongside QuadrupedPyMPC, while Chalito is described as reading a fixed 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