5 papers
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…
Multi-Sensor Fusion for Quadruped Robot State Estimation using Invariant Filtering and Smoothing
Ylenia Nisticò, Hajun Kim, João Carlos Virgolino Soares +3
This letter introduces two multi-sensor state estimation frameworks for quadruped robots, built on the Invariant Extended Kalman Filter (InEKF) and Invariant Smoother (IS). The pro…
MUSE: A Real-Time Multi-Sensor State Estimator for Quadruped Robots
Ylenia Nisticò, João Carlos Virgolino Soares, Lorenzo Amatucci +2
This paper introduces an innovative state estimator, MUSE (MUlti-sensor State Estimator), designed to enhance state estimation's accuracy and real-time performance in quadruped rob…
Proprioceptive State Estimation for Quadruped Robots using Invariant Kalman Filtering and Scale-Variant Robust Cost Functions
Hilton Marques Souza Santana, João Carlos Virgolino Soares, Ylenia Nisticò +2
Accurate state estimation is crucial for legged robot locomotion, as it provides the necessary information to allow control and navigation. However, it is also challenging, especia…