Sensor fusion for smartphone pose estimation

With the increasing sophistication of smartphone applications, accurate positioning and tracking have become a vital component of various cutting-edge technologies where sensor fusion is essential in the integration of data from multiple sensors to achieve a more accurate and reliable estimation of...

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Main Author: Ho, Kok Pin
Other Authors: Arokiaswami Alphones
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2023
Subjects:
Online Access:https://hdl.handle.net/10356/166838
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1668382023-07-07T15:54:05Z Sensor fusion for smartphone pose estimation Ho, Kok Pin Arokiaswami Alphones School of Electrical and Electronic Engineering EAlphones@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering With the increasing sophistication of smartphone applications, accurate positioning and tracking have become a vital component of various cutting-edge technologies where sensor fusion is essential in the integration of data from multiple sensors to achieve a more accurate and reliable estimation of the state of a system. This project proposes an innovative approach to improve the performance of Kalman filter algorithms, specifically, the Enhanced Error-State Kalman Filter (ESKF), for smartphone pose estimation. The ESKF method is a more efficient and robust alternative to the commonly used Extended Kalman Filter (EKF) method, particularly for nonlinear systems. ESKF requires prior construction of an error process that relates the error variables associated with the pose estimation system model. Besides, a magnetometer correction model is introduced which enables better magnetic tolerance and compensation than the conventional IMU filter. The results demonstrate that the enhanced ESKF method achieves remarkable precision in estimating three-dimensional rotations with error typically below 5° under a non-disturbed environment. Bachelor of Engineering (Electrical and Electronic Engineering) 2023-05-10T01:43:53Z 2023-05-10T01:43:53Z 2023 Final Year Project (FYP) Ho, K. P. (2023). Sensor fusion for smartphone pose estimation. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166838 https://hdl.handle.net/10356/166838 en A3048-221 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::Computer science and engineering::Computing methodologies::Simulation and modeling
Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
spellingShingle Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling
Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
Ho, Kok Pin
Sensor fusion for smartphone pose estimation
description With the increasing sophistication of smartphone applications, accurate positioning and tracking have become a vital component of various cutting-edge technologies where sensor fusion is essential in the integration of data from multiple sensors to achieve a more accurate and reliable estimation of the state of a system. This project proposes an innovative approach to improve the performance of Kalman filter algorithms, specifically, the Enhanced Error-State Kalman Filter (ESKF), for smartphone pose estimation. The ESKF method is a more efficient and robust alternative to the commonly used Extended Kalman Filter (EKF) method, particularly for nonlinear systems. ESKF requires prior construction of an error process that relates the error variables associated with the pose estimation system model. Besides, a magnetometer correction model is introduced which enables better magnetic tolerance and compensation than the conventional IMU filter. The results demonstrate that the enhanced ESKF method achieves remarkable precision in estimating three-dimensional rotations with error typically below 5° under a non-disturbed environment.
author2 Arokiaswami Alphones
author_facet Arokiaswami Alphones
Ho, Kok Pin
format Final Year Project
author Ho, Kok Pin
author_sort Ho, Kok Pin
title Sensor fusion for smartphone pose estimation
title_short Sensor fusion for smartphone pose estimation
title_full Sensor fusion for smartphone pose estimation
title_fullStr Sensor fusion for smartphone pose estimation
title_full_unstemmed Sensor fusion for smartphone pose estimation
title_sort sensor fusion for smartphone pose estimation
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/166838
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