9 papers
Chalito: An Extensible Library for Filtering-Based State Estimation in Quadruped Robots
Hilton Marques Souza Santana, João Carlos Virgolino Soares, Marco Antonio Meggiolaro +1
State estimation is essential for quadruped robots, enabling robust locomotion, navigation, and control. While many estimators have been proposed in the literature, existing implem…
Iterated Invariant EKF for 3D Landmark-Aided Inertial Navigation
Hilton Marques Souza Santana, João Carlos Virgolino Soares, Marco Antonio Meggiolaro
Inertial navigation systems aided by three-dimensional landmark measurements constitute a fundamental problem in robotic perception and state estimation. Classical SO(3)-based Exte…
A Proprioceptive-Only Benchmark for Quadruped State Estimation: ATE, RPE, and Runtime Trade-offs Between Filters and Smoothers
Ylenia Nisticò, João Carlos Virgolino Soares, Joan Solà +1
We compare three state-of-the-art proprioceptive state estimators for quadruped robots: MUSE [1], the Invariant Extended Kalman Filter (IEKF) [2], and the Invariant Smoother (IS) […
Iterated Invariant EKF for Quadruped Robot Odometry
Hilton Marques Souza Santana, João Carlos Virgolino Soares, Sven Goffin +4
Kalman filter-based algorithms are fundamental for mobile robots, as they provide a computationally efficient solution to the challenging problem of state estimation. However, they…
Proprioceptive Image: An Image Representation of Proprioceptive Data from Quadruped Robots for Contact Estimation Learning
Gabriel Fischer Abati, João Carlos Virgolino Soares, Giulio Turrisi +2
This paper presents a novel approach for representing proprioceptive time-series data from quadruped robots as structured two-dimensional images, enabling the use of convolutional…
VAR-SLAM: Visual Adaptive and Robust SLAM for Dynamic Environments
João Carlos Virgolino Soares, Gabriel Fischer Abati, Claudio Semini
Visual SLAM in dynamic environments remains challenging, as several existing methods rely on semantic filtering that only handles known object classes, or use fixed robust kernels…