Publication Date

2024

Document Type

Dissertation/Thesis

First Advisor

Ryu, Ji-Chul

Degree Name

M.S. (Master of Science)

Legacy Department

Department of Mechanical Engineering

Abstract

Localization, or state-estimation algorithms, are one of the most important aspects inthe development of autonomous mobile robots. Typical localization requires an IMU (Inertial Measurement Unit) along with an external reference, such as GPS (Global Positioning System) for outdoor applications. In indoor applications, the GPS data is not accessible so many mobile robot implementations turn to magnetometers to provide additional pose information. However, in the context of miniaturizing robotic systems, magnetometers are not always reliable due to their proximity to motors and other electronics, causing magnetic distortion and in turn, incorrect pose information. To address this issue, this thesis proposes a magnetometer-less state-estimation algorithm based on a cascaded Extended Kalman Filter (EKF) framework for a unicycle model mobile robot. The algorithm consists of two cascaded Kalman filters. The first is the unicycle model-based EKF that estimates the position and heading of a mobile robot using error-compensated IMU measurements. The second is the IMU bias estimation EKF that uses estimated heading from the first filter as well as velocity from the IMU as measurement. To minimize noise from sensor data and error accumulation from numerical integration, zero velocity update (ZUPT) methods were used. In case reference data is available, a gradient descent based optimization method is used for fusion and correction. The algorithm was tested both in simulation and experiments to prove the validity. The experimental results showed that the proposed algorithm is able to compensate for the issue of sensor drift, and provide improved estimation even without additional sensors. The framework developed and results collected set the stage for future work in estimation using cascaded Kalman filters.

Extent

92 pages

Language

en

Publisher

Northern Illinois University

Rights Statement

In Copyright

Rights Statement 2

NIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.

Media Type

Text

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Robotics Commons

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