Sensor Fusion for Localization of Automated Guided Vehicles

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master_Sari_Onur_2020.pdf (3.53 MB)

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Sähkötekniikan korkeakoulu | Master's thesis
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Date

2020-10-19

Department

Major/Subject

Control, Robotics and Autonomous Systems

Mcode

ELEC3025

Degree programme

AEE - Master’s Programme in Automation and Electrical Engineering (TS2013)

Language

en

Pages

92

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Abstract

Automated Guided Vehicles (AGVs) need to localize themselves reliably in order to perform their tasks efficiently. To that end, they rely on noisy sensor measurements that potentially provide erroneous location estimates if they are used directly. To prevent this issue, measurements from different kinds of sensors are generally used together. This thesis presents a Kalman Filter based sensor fusion approach that is able to function with asynchronous measurements from laser scanners, odometry and Inertial Measurement Units (IMUs). The method uses general kinematic equations for state prediction that work with any type of vehicle kinematics and utilizes state augmentation to estimate gyroscope and accelerometer biases. The developed algorithm was tested with an open source multisensor navigation dataset and real-time experiments with an AGV. In both sets of experiments, scenarios in which the laser scanner was fully available, partially available or not available were compared. It was found that using sensor fusion resulted in a smaller deviation from the actual trajectory compared to using only a laser scanner. Furthermore, in each experiment, using sensor fusion decreased the localization error in the time periods where the laser was unavailable, although the amount of improvement depended on the duration of unavailability and motion characteristics

Description

Supervisor

Kyrki, Ville

Thesis advisor

Tariq, Usama

Keywords

sensor fusion, kalman filter, automated guided vehicle, navigation

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https://urn.fi/URN:NBN:fi:aalto-202010255981

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[dipl] Sähkötekniikan korkeakoulu / ELEC