Localization of an underwater robot for remote data collection

Underwater localization is a fundamental requirement in underwater robotics. Accurate and drift free pose estimates are required for navigation and operation in challenging underwater environments. Inaccuracies and errors in localization would result in the degradation of the data collected. In this...

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Main Author: Wee, Mervyn Wei Jie
Other Authors: Hu, Guoqiang
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2020
Subjects:
Online Access:https://hdl.handle.net/10356/145445
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1454452023-07-07T16:59:25Z Localization of an underwater robot for remote data collection Wee, Mervyn Wei Jie Hu, Guoqiang School of Electrical and Electronic Engineering GQHu@ntu.edu.sg Engineering::Electrical and electronic engineering Underwater localization is a fundamental requirement in underwater robotics. Accurate and drift free pose estimates are required for navigation and operation in challenging underwater environments. Inaccuracies and errors in localization would result in the degradation of the data collected. In this work, a low-cost localization method using the inertial measurement unit (IMU) and an Extended Kalman Filter (EKF) algorithm is proposed for miniature underwater robots. The underwater robot used in this work is the open source Remotely Operated Vehicle (openROV). The proposed method uses data from the IMU of the openROV robot and inputs it into the EKF algorithm to create a map which shows the position and path of the openROV robot. The fundamental algorithmic principles behind the localization technique is described, and the algorithm is tested using a dataset and a simulation. Simulation results show that the designed algorithm can achieve accurate pose estimation. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-12-22T01:54:50Z 2020-12-22T01:54:50Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/145445 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
spellingShingle Engineering::Electrical and electronic engineering
Wee, Mervyn Wei Jie
Localization of an underwater robot for remote data collection
description Underwater localization is a fundamental requirement in underwater robotics. Accurate and drift free pose estimates are required for navigation and operation in challenging underwater environments. Inaccuracies and errors in localization would result in the degradation of the data collected. In this work, a low-cost localization method using the inertial measurement unit (IMU) and an Extended Kalman Filter (EKF) algorithm is proposed for miniature underwater robots. The underwater robot used in this work is the open source Remotely Operated Vehicle (openROV). The proposed method uses data from the IMU of the openROV robot and inputs it into the EKF algorithm to create a map which shows the position and path of the openROV robot. The fundamental algorithmic principles behind the localization technique is described, and the algorithm is tested using a dataset and a simulation. Simulation results show that the designed algorithm can achieve accurate pose estimation.
author2 Hu, Guoqiang
author_facet Hu, Guoqiang
Wee, Mervyn Wei Jie
format Final Year Project
author Wee, Mervyn Wei Jie
author_sort Wee, Mervyn Wei Jie
title Localization of an underwater robot for remote data collection
title_short Localization of an underwater robot for remote data collection
title_full Localization of an underwater robot for remote data collection
title_fullStr Localization of an underwater robot for remote data collection
title_full_unstemmed Localization of an underwater robot for remote data collection
title_sort localization of an underwater robot for remote data collection
publisher Nanyang Technological University
publishDate 2020
url https://hdl.handle.net/10356/145445
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