Simone Godio

This paper describes a sensor fusion technique to localize autonomously unmanned vehicles. In particular, we performed a sensor fusion based on the extended Kalman filter between two commercial sensors. The adopted sensors are ZED2 and Intel T265, respectively; these platforms already perform visual-inertial odometry in their integrated system-on-chip. Since these 2 devices represent the top of the range on the market to make an autonomous localization, this study aims to analyze and inform about results that can be obtained by performing a sensor fusion between the two cameras.

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[1] Simone Godio, "Visual Inertial Odometry Sensor Fusion Approach for Autonomous Localization", IEEE Dataport, 2021. [Online]. Available: http://dx.doi.org/10.21227/10av-kj18. Accessed: Jan. 19, 2025.
@data{10av-kj18-21,
doi = {10.21227/10av-kj18},
url = {http://dx.doi.org/10.21227/10av-kj18},
author = {Simone Godio },
publisher = {IEEE Dataport},
title = {Visual Inertial Odometry Sensor Fusion Approach for Autonomous Localization},
year = {2021} }
TY - DATA
T1 - Visual Inertial Odometry Sensor Fusion Approach for Autonomous Localization
AU - Simone Godio
PY - 2021
PB - IEEE Dataport
UR - 10.21227/10av-kj18
ER -
Simone Godio. (2021). Visual Inertial Odometry Sensor Fusion Approach for Autonomous Localization. IEEE Dataport. http://dx.doi.org/10.21227/10av-kj18
Simone Godio, 2021. Visual Inertial Odometry Sensor Fusion Approach for Autonomous Localization. Available at: http://dx.doi.org/10.21227/10av-kj18.
Simone Godio. (2021). "Visual Inertial Odometry Sensor Fusion Approach for Autonomous Localization." Web.
1. Simone Godio. Visual Inertial Odometry Sensor Fusion Approach for Autonomous Localization [Internet]. IEEE Dataport; 2021. Available from : http://dx.doi.org/10.21227/10av-kj18
Simone Godio. "Visual Inertial Odometry Sensor Fusion Approach for Autonomous Localization." doi: 10.21227/10av-kj18