Ylenia Nisticò — accepted AI-conference papers

2025

MUSE: A Real-Time Multi-Sensor State Estimator for Quadruped Robots

Ylenia Nisticò, João Carlos Virgolino Soares, Lorenzo Amatucci, Geoff Fink, Claudio Semini

RA-L 2025

This letter introduces an innovative state estimator, MUSE (MUlti-sensor State Estimator), designed to enhance state estimation's accuracy and real-time performance in quadruped robot navigation. The proposed state estimator builds upon our previous work presented in (Fink et al. 2020). It integrate

Cited by 11 Source ↗Scholar ↗

2025

Multi-Sensor Fusion for Quadruped Robot State Estimation Using Invariant Filtering and Smoothing

Ylenia Nisticò, Hajun Kim, João Carlos Virgolino Soares, Geoff Fink, Hae-Won Park, Claudio Semini

RA-L 2025

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 proposed methods, named E-InEKF and E-IS, fuse kinematics, IMU, LiDAR, and GPS data to mitigate position drift, particularl

Cited by 3 Source ↗Scholar ↗