Hybrid learning for quadrotor navigation and sensor fusion

Itzik Klein

Hybrid learning for quadrotor navigation and sensor fusion

Department of Marine Technologies

Research output: Contribution to conference › Paper › peer-review

Abstract

Obtaining accurate outdoor quadrotor navigation commonly requires fusion between several sensors, such as inertial sensors and global navigation satellite systems, in a model-based, nonlinear estimation framework. Recently, data-driven approaches applied in various fields show state-of-The-Art performance, compared to model-based methods. In this paper, we present two hybrid-learning approaches for quadrotor navigation and provide field experiments analysis and results.

Original language	English
State	Published - 2023
Event	62nd Israel Annual Conference on Aerospace Sciences, IACAS 2023 - Haifa, Israel Duration: 15 Mar 2023 → 16 Mar 2023

Conference

Conference	62nd Israel Annual Conference on Aerospace Sciences, IACAS 2023
Country/Territory	Israel
City	Haifa
Period	15/03/23 → 16/03/23

Bibliographical note

Publisher Copyright:
© IACAS 2023. All rights reserved.

ASJC Scopus subject areas

Aerospace Engineering

Cite this

@conference{521f251b729b49dfa15d29eef6f29e7f,

title = "Hybrid learning for quadrotor navigation and sensor fusion",

abstract = "Obtaining accurate outdoor quadrotor navigation commonly requires fusion between several sensors, such as inertial sensors and global navigation satellite systems, in a model-based, nonlinear estimation framework. Recently, data-driven approaches applied in various fields show state-of-The-Art performance, compared to model-based methods. In this paper, we present two hybrid-learning approaches for quadrotor navigation and provide field experiments analysis and results.",

author = "Itzik Klein",

year = "2023",

language = "English",

}

TY - CONF

T1 - Hybrid learning for quadrotor navigation and sensor fusion

AU - Klein, Itzik

PY - 2023

Y1 - 2023

N2 - Obtaining accurate outdoor quadrotor navigation commonly requires fusion between several sensors, such as inertial sensors and global navigation satellite systems, in a model-based, nonlinear estimation framework. Recently, data-driven approaches applied in various fields show state-of-The-Art performance, compared to model-based methods. In this paper, we present two hybrid-learning approaches for quadrotor navigation and provide field experiments analysis and results.

AB - Obtaining accurate outdoor quadrotor navigation commonly requires fusion between several sensors, such as inertial sensors and global navigation satellite systems, in a model-based, nonlinear estimation framework. Recently, data-driven approaches applied in various fields show state-of-The-Art performance, compared to model-based methods. In this paper, we present two hybrid-learning approaches for quadrotor navigation and provide field experiments analysis and results.

UR - https://www.scopus.com/pages/publications/85192571801

M3 - Paper

AN - SCOPUS:85192571801

T2 - 62nd Israel Annual Conference on Aerospace Sciences, IACAS 2023

Y2 - 15 March 2023 through 16 March 2023

ER -

Hybrid learning for quadrotor navigation and sensor fusion

Abstract

Conference

Bibliographical note

ASJC Scopus subject areas

Other files and links

Fingerprint

Cite this