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Inertial sensor self-calibration in a visually-aided navigation approach for a micro-AUV

Inertial sensor self-calibration in a visually-aided navigation approach for a micro-AUV
Inertial sensor self-calibration in a visually-aided navigation approach for a micro-AUV
This paper presents a new solution for underwater observation, image recording, mapping and 3D reconstruction in shallow waters. The platform, designed as a research and testing tool, is based on a small underwater robot equipped with a MEMS-based IMU, two stereo cameras and a pressure sensor. The data given by the sensors are fused, adjusted and corrected in a multiplicative error state Kalman filter (MESKF), which returns a single vector with the pose and twist of the vehicle and the biases of the inertial sensors (the accelerometer and the gyroscope). The inclusion of these biases in the state vector permits their self-calibration and stabilization, improving the estimates of the robot orientation. Experiments in controlled underwater scenarios and in the sea have demonstrated a satisfactory performance and the capacity of the vehicle to operate in real environments and in real time.
Autonomous underwater vehicles, Sensor fusion, Underwater landscape, Visual localization
1825-1860
MDPI AG
Bonin-Font, Francisco
c5618f01-7ab3-440c-9551-f2db395d82c3
Massot-Campos, Miquel
a55d7b32-c097-4adf-9483-16bbf07f9120
Negre-Carrasco, Pep Lluis
c81e0846-e76b-466d-8bfd-c49251a7a7e5
Oliver-Codina, Gabriel
99a9b816-4f5d-4724-8525-aa7c45fb0d3d
Beltran, Joan P.
debbc508-4272-45d5-937f-b8d2ff6f0e14
Bonin-Font, Francisco
c5618f01-7ab3-440c-9551-f2db395d82c3
Massot-Campos, Miquel
a55d7b32-c097-4adf-9483-16bbf07f9120
Negre-Carrasco, Pep Lluis
c81e0846-e76b-466d-8bfd-c49251a7a7e5
Oliver-Codina, Gabriel
99a9b816-4f5d-4724-8525-aa7c45fb0d3d
Beltran, Joan P.
debbc508-4272-45d5-937f-b8d2ff6f0e14

Bonin-Font, Francisco, Massot-Campos, Miquel, Negre-Carrasco, Pep Lluis, Oliver-Codina, Gabriel and Beltran, Joan P. (2015) Inertial sensor self-calibration in a visually-aided navigation approach for a micro-AUV. In, Sensors (Switzerland). (Sensors (Switzerland), , (doi:10.3390/s150101825), 15) MDPI AG, pp. 1825-1860. (doi:10.3390/s150101825).

Record type: Book Section

Abstract

This paper presents a new solution for underwater observation, image recording, mapping and 3D reconstruction in shallow waters. The platform, designed as a research and testing tool, is based on a small underwater robot equipped with a MEMS-based IMU, two stereo cameras and a pressure sensor. The data given by the sensors are fused, adjusted and corrected in a multiplicative error state Kalman filter (MESKF), which returns a single vector with the pose and twist of the vehicle and the biases of the inertial sensors (the accelerometer and the gyroscope). The inclusion of these biases in the state vector permits their self-calibration and stabilization, improving the estimates of the robot orientation. Experiments in controlled underwater scenarios and in the sea have demonstrated a satisfactory performance and the capacity of the vehicle to operate in real environments and in real time.

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More information

Published date: 16 January 2015
Keywords: Autonomous underwater vehicles, Sensor fusion, Underwater landscape, Visual localization

Identifiers

Local EPrints ID: 428718
URI: https://eprints.soton.ac.uk/id/eprint/428718
PURE UUID: a46e0699-5217-4c1c-94e0-be150cd5ea4b

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Date deposited: 07 Mar 2019 17:30
Last modified: 07 Mar 2019 17:30

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Contributors

Author: Francisco Bonin-Font
Author: Pep Lluis Negre-Carrasco
Author: Gabriel Oliver-Codina
Author: Joan P. Beltran

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