Morten C. Nissov — accepted AI-conference papers

2026

Equivariant Filter for Radar-Inertial Odometry

Giulio Delama, Jan Michalczyk, Morten C. Nissov, Martin Scheiber, Alessandro Fornasier, Kostas Alexis +1

RA-L 2026

Radar-Inertial Odometry (RIO) based on the Extended Kalman Filter (EKF) relies on accurate extrinsic calibration between the radar and the Inertial Measurement Unit (IMU) and is sensitive to disturbances, as large linearization errors can degrade performance or even cause divergence. To address thes

Cited by 0 Source ↗Scholar ↗

2024

Robust High-Speed State Estimation for Off-Road Navigation Using Radar Velocity Factors

Morten C. Nissov, Jeffrey A. Edlund, Patrick Spieler, Curtis Padgett, Kostas Alexis, Shehryar Khattak

RA-L 2024

Enabling robot autonomy in complex environments for mission critical application requires robust state estimation. Particularly under conditions where the exteroceptive sensors, which the navigation depends on, can be degraded by environmental challenges thus, leading to mission failure. It is preci

Cited by 9 Source ↗Scholar ↗