Real-time implementation of airborne inertial-SLAM

Jonghyuk Kim; Salah Sukkarieh

doi:10.1016/j.robot.2006.06.006

Real-time implementation of airborne inertial-SLAM

Jonghyuk Kim^*, Salah Sukkarieh

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

151 Citations (Scopus)

Abstract

This paper addresses some challenges to the real-time implementation of Simultaneous Localisation and Mapping (SLAM) on a UAV platform. When compared to the implementation of SLAM in 2D environments, airborne implementation imposes several difficulties in terms of computational complexity and loop closure, with high nonlinearity in both vehicle dynamics and observations. An implementation of airborne SLAM is formulated to relieve this computational complexity in both direct and indirect ways. Our implementation is based on an Extended Kalman Filter (EKF), which fuses data from an Inertial Measurement Unit (IMU) with data from a passive vision system. Real-time results from flight trials are provided.

Original language	English
Pages (from-to)	62-71
Number of pages	10
Journal	Robotics and Autonomous Systems
Volume	55
Issue number	1
DOIs	https://doi.org/10.1016/j.robot.2006.06.006
Publication status	Published - 31 Jan 2007

Access to Document

10.1016/j.robot.2006.06.006

Cite this

@article{5b8692f7c9e04eec80a48f32732aa054,

title = "Real-time implementation of airborne inertial-SLAM",

abstract = "This paper addresses some challenges to the real-time implementation of Simultaneous Localisation and Mapping (SLAM) on a UAV platform. When compared to the implementation of SLAM in 2D environments, airborne implementation imposes several difficulties in terms of computational complexity and loop closure, with high nonlinearity in both vehicle dynamics and observations. An implementation of airborne SLAM is formulated to relieve this computational complexity in both direct and indirect ways. Our implementation is based on an Extended Kalman Filter (EKF), which fuses data from an Inertial Measurement Unit (IMU) with data from a passive vision system. Real-time results from flight trials are provided.",

keywords = "Airborne SLAM, Inertial Measurement Unit (IMU), UAV, Vision",

author = "Jonghyuk Kim and Salah Sukkarieh",

year = "2007",

month = jan,

day = "31",

doi = "10.1016/j.robot.2006.06.006",

language = "English",

volume = "55",

pages = "62--71",

journal = "Robotics and Autonomous Systems",

issn = "0921-8890",

publisher = "Elsevier B.V.",

number = "1",

}

TY - JOUR

T1 - Real-time implementation of airborne inertial-SLAM

AU - Kim, Jonghyuk

AU - Sukkarieh, Salah

PY - 2007/1/31

Y1 - 2007/1/31

N2 - This paper addresses some challenges to the real-time implementation of Simultaneous Localisation and Mapping (SLAM) on a UAV platform. When compared to the implementation of SLAM in 2D environments, airborne implementation imposes several difficulties in terms of computational complexity and loop closure, with high nonlinearity in both vehicle dynamics and observations. An implementation of airborne SLAM is formulated to relieve this computational complexity in both direct and indirect ways. Our implementation is based on an Extended Kalman Filter (EKF), which fuses data from an Inertial Measurement Unit (IMU) with data from a passive vision system. Real-time results from flight trials are provided.

AB - This paper addresses some challenges to the real-time implementation of Simultaneous Localisation and Mapping (SLAM) on a UAV platform. When compared to the implementation of SLAM in 2D environments, airborne implementation imposes several difficulties in terms of computational complexity and loop closure, with high nonlinearity in both vehicle dynamics and observations. An implementation of airborne SLAM is formulated to relieve this computational complexity in both direct and indirect ways. Our implementation is based on an Extended Kalman Filter (EKF), which fuses data from an Inertial Measurement Unit (IMU) with data from a passive vision system. Real-time results from flight trials are provided.

KW - Airborne SLAM

KW - Inertial Measurement Unit (IMU)

KW - UAV

KW - Vision

UR - http://www.scopus.com/inward/record.url?scp=33751160829&partnerID=8YFLogxK

U2 - 10.1016/j.robot.2006.06.006

DO - 10.1016/j.robot.2006.06.006

M3 - Article

SN - 0921-8890

VL - 55

SP - 62

EP - 71

JO - Robotics and Autonomous Systems

JF - Robotics and Autonomous Systems

IS - 1

ER -

Real-time implementation of airborne inertial-SLAM

Abstract

Access to Document

Other files and links

Fingerprint

Cite this