Farhad Shamsfakhr — accepted AI-conference papers

2023

UVIO: An UWB-Aided Visual-Inertial Odometry Framework with Bias-Compensated Anchors Initialization

Giulio Delama, Farhad Shamsfakhr, Stephan Weiss, Daniele Fontanelli, Alessandro Fomasier

IROS 2023poster

This paper introduces UVIO, a multi-sensor framework that leverages Ultra Wide Band (UWB) technology and Visual-Inertial Odometry (VIO) to provide robust and low-drift localization. In order to include range measurements in state estimation, the position of the UWB anchors must be known. This study…

Cited by 8 Source ↗Scholar ↗

2021

UWB Indoor Global Localisation for Nonholonomic Robots with Unknown Offset Compensation

Daniele Fontanelli, Farhad Shamsfakhr, Paolo Bevilacqua, Luigi Palopoli

ICRA 2021poster

The problem addressed in this paper is the localisation of a mobile robot using a combination of on-board sensors and Ultra-Wideband (UWB) beacons. Specifically, we consider a scenario in which a mobile robot travels across an area infrastructured with a small number of UWB anchors. The presence of…

Cited by 11 Source ↗Scholar ↗