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Computationally efficient visual-inertial sensor fusion for GPS-denied navigation on a small quadrotor

Computationally efficient visual-inertial sensor fusion for GPS-denied navigation on a small quadrotor
Computationally efficient visual-inertial sensor fusion for GPS-denied navigation on a small quadrotor
Because of the complementary nature of visual and inertial sensors, the combination of both is able to provide accurate and fast six degree-of-freedom (DOF) state estimation, which is the fundamental requirement for robotic (especially unmanned aerial vehicle) navigation tasks in GPS-denied environments. This paper presents a computationally efficient visual-inertial fusion algorithm, by separating orientation fusion from the position fusion process. It is designed to perform 6 DOF state estimation, based on a gyroscope, an accelerometer and a monocular visual-based simultaneous localization and mapping (mSLAM) algorithm measurement. It also recovers the visual scale for the mSLAM. In particular, the fusion algorithm treats the orientation fusion and position fusion as two separate processes, where the orientation fusion is based on a very efficient gradient descent algorithm, and position fusion is based on a 13-state linear Kalman filter. The elimination of a magnetometer avoids the problem of magnetic distortion, which makes it a power-on-and-go system once the gyroscope and accelerometer are factory calibrated. The resulting algorithm shows a significant computation reduction over the conventional extended Kalman filter with competitive accuracy. Moreover, the separation between the orientation and position fusion process enables the algorithm to be easily implemented into separate hardware, thus allowing the two fusion processes to be executed concurrently
Liu, Chang
7c245137-3dbb-41c6-84e6-e40181b25a0d
Prior, Stephen
9c753e49-092a-4dc5-b4cd-6d5ff77e9ced
Liu, Chang
7c245137-3dbb-41c6-84e6-e40181b25a0d
Prior, Stephen
9c753e49-092a-4dc5-b4cd-6d5ff77e9ced

Liu, Chang and Prior, Stephen (2015) Computationally efficient visual-inertial sensor fusion for GPS-denied navigation on a small quadrotor. 2015 International Conference on Innovation, Communication and Engineering, Xiangtan, China. 23 - 28 Oct 2015.

Record type: Conference or Workshop Item (Paper)

Abstract

Because of the complementary nature of visual and inertial sensors, the combination of both is able to provide accurate and fast six degree-of-freedom (DOF) state estimation, which is the fundamental requirement for robotic (especially unmanned aerial vehicle) navigation tasks in GPS-denied environments. This paper presents a computationally efficient visual-inertial fusion algorithm, by separating orientation fusion from the position fusion process. It is designed to perform 6 DOF state estimation, based on a gyroscope, an accelerometer and a monocular visual-based simultaneous localization and mapping (mSLAM) algorithm measurement. It also recovers the visual scale for the mSLAM. In particular, the fusion algorithm treats the orientation fusion and position fusion as two separate processes, where the orientation fusion is based on a very efficient gradient descent algorithm, and position fusion is based on a 13-state linear Kalman filter. The elimination of a magnetometer avoids the problem of magnetic distortion, which makes it a power-on-and-go system once the gyroscope and accelerometer are factory calibrated. The resulting algorithm shows a significant computation reduction over the conventional extended Kalman filter with competitive accuracy. Moreover, the separation between the orientation and position fusion process enables the algorithm to be easily implemented into separate hardware, thus allowing the two fusion processes to be executed concurrently

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Published date: 23 October 2015
Venue - Dates: 2015 International Conference on Innovation, Communication and Engineering, Xiangtan, China, 2015-10-23 - 2015-10-28
Organisations: Computational Engineering & Design Group

Identifiers

Local EPrints ID: 383925
URI: http://eprints.soton.ac.uk/id/eprint/383925
PURE UUID: 388225c6-ec3f-4d82-b4df-c47628223e21
ORCID for Chang Liu: ORCID iD orcid.org/0000-0002-6967-5159
ORCID for Stephen Prior: ORCID iD orcid.org/0000-0002-4993-4942

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Date deposited: 27 Nov 2015 16:38
Last modified: 15 Mar 2024 03:45

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Contributors

Author: Chang Liu ORCID iD
Author: Stephen Prior ORCID iD

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