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Framläggning av examensarbeten / Thesis presentations


WExUpp - kommande framläggningar
2017-11-24 - Datorteknik
Implementation of SLAM Algorithms in a Small-Scale Vehicle Using Model-Based Development
Johan Alexandersson, Olle Nordin
Avancerad (30hp)
kl 15:15, Algoritmen (På svenska)
[Abstract]
As autonomous driving is rapidly becoming the next major challenge in the auto- motive industry, the problem of Simultaneous Localization And Mapping (SLAM) has never been more relevant than it is today. This thesis presents the idea of examining SLAM algorithms by implementing such an algorithm on a radio con- trolled car which has been fitted with sensors and microcontrollers. The software architecture of this small-scale vehicle is based on the Robot Operating System (ROS), an open-source framework designed to be used in robotic applications.

This thesis covers Extended Kalman Filter (EKF)-based SLAM, FastSLAM, and GraphSLAM, examining these algorithms in both theoretical investigations, simulations, and real-world experiments. The method used in this thesis is model- based development, meaning that a model of the vehicle is first implemented in order to be able to perform simulations using each algorithm. A decision of which algorithm to be implemented on the physical vehicle is then made backed up by these simulation results, as well as a theoretical investigation of each algorithm.

This thesis has resulted in a dynamic model of a small-scale vehicle which can be used for simulation of any ROS-compliant SLAM-algorithm, and this model has been simulated extensively in order to provide empirical evidence to define which SLAM algorithm is most suitable for this application. Out of the algo- rithms examined, FastSLAM was proven to the best candidate, and was in the final stage, through usage of the ROS package gMapping, successfully imple- mented on the small-scale vehicle.
2017-11-30 - Datorseende
Reading Barcodes with Neural Networks
Fredrik Fridborn
Avancerad (30hp)
kl 13:00, Transformen (På svenska)
[Abstract]
Barcodes are ubiquituous in modern society and they have had industrial application for decades. However, for noisy images modern methods can underperform. Poor lighting conditions, occlusions and low resolution can be problematic in decoding. This thesis aims to solve this problem by using neural networks, which have enjoyed great success in many computer vision competitions the last years. We investigate how three different networks perform on data sets with noisy images. The first network is a single classifier, the second network is an ensemble classifier and the third is based on a pre-trained feature extractor. For comparison, we also test two baseline methods that are used in industry today.We generate training data using software and modify it to ensure proper generalization. Testing data is created by photographing barcodes in different settings, creating six image classes - normal, dark, white, rotated, occluded and wrinkled.The proposed single classifier and ensemble classifier outperform the baseline as well as the pre-trained feature extractor by a large margin. The thesis work was performed at SICK IVP, a machine vision company in Linköping in 2017.
2017-12-05 - Datorseende
Semantic Segmentation of Three-Dimensional Urban Scene Models
Johan Lind
Avancerad (30hp)
kl 09:00, Systemet (På svenska)
2017-12-15 - Reglerteknik
Clustering for Multi-Target Tracking
Jonas Hyllengren
Avancerad (30hp)
kl 09:00, Systemet (På svenska)

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