This paper introduces UVIO, a multi-sensor framework that leverages uwb technology and 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 proposes a multi-step initialization procedure to map multiple unknown anchors by an uav, in a fully autonomous fashion. To address the limitations of initializing uwb anchors via a random trajectory, this paper uses the gdop as a measure of optimality in anchor position estimation, to compute a set of optimal waypoints and synthesize a trajectory that minimizes the mapping uncertainty. After the initialization is complete, the range measurements from multiple anchors, including measurement biases, are tightly integrated into the vio system. While in range of the initialized anchors, the vio drift in position and heading is eliminated. The effectiveness of UVIO and our initialization procedure has been validated through a series of simulations and real-world experiments.

UVIO: An UWB-Aided Visual-Inertial Odometry Framework with Bias-Compensated Anchors Initialization / Delama, Giulio; Shamsfakhr, Farhad; Weiss, Stephan; Fontanelli, Daniele; Fomasier, Alessandro. - (2023), pp. 7111-7118. (Intervento presentato al convegno IROS23 tenutosi a Detroit, MI, USA nel 1st-5th October 2023) [10.1109/iros55552.2023.10342012].

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

Shamsfakhr, Farhad
Secondo
;
Fontanelli, Daniele
Penultimo
;
2023-01-01

Abstract

This paper introduces UVIO, a multi-sensor framework that leverages uwb technology and 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 proposes a multi-step initialization procedure to map multiple unknown anchors by an uav, in a fully autonomous fashion. To address the limitations of initializing uwb anchors via a random trajectory, this paper uses the gdop as a measure of optimality in anchor position estimation, to compute a set of optimal waypoints and synthesize a trajectory that minimizes the mapping uncertainty. After the initialization is complete, the range measurements from multiple anchors, including measurement biases, are tightly integrated into the vio system. While in range of the initialized anchors, the vio drift in position and heading is eliminated. The effectiveness of UVIO and our initialization procedure has been validated through a series of simulations and real-world experiments.
2023
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Piscataway, New Jersey
IEEE
978-1-6654-9190-7
Delama, Giulio; Shamsfakhr, Farhad; Weiss, Stephan; Fontanelli, Daniele; Fomasier, Alessandro
UVIO: An UWB-Aided Visual-Inertial Odometry Framework with Bias-Compensated Anchors Initialization / Delama, Giulio; Shamsfakhr, Farhad; Weiss, Stephan; Fontanelli, Daniele; Fomasier, Alessandro. - (2023), pp. 7111-7118. (Intervento presentato al convegno IROS23 tenutosi a Detroit, MI, USA nel 1st-5th October 2023) [10.1109/iros55552.2023.10342012].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/403549
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