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An exploratory research of ARCore's feature detection
KTH, School of Electrical Engineering and Computer Science (EECS).
KTH, School of Electrical Engineering and Computer Science (EECS).
2018 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Augmented reality has been on the rise for some time now and begun making its way onto the mobile market for both IOS and Android. In 2017 Apple released ARKit for IOS which is a software development kit for developing augmented reality applications. To counter this, Google released their own variant called ARCore on the 1st of march 2018. ARCore is also a software development kit for developing augmented reality applications but made for the Android, Unity and Unreal platforms instead. Since ARCore is released recently it is still unknown what particular limitations may exist for it. The purpose of this paper is give an indication to companies and developers about ARCore’s potential limitations. The goal with this paper and work is to map how well ARCore works during different circumstances, and in particular, how its feature detection works and behaves.

A quantitative research was done with the usage of the case study method. Various tests were performed with a modified test-application supplied by Google. The tests included testing how ARCore’s feature detection, the process that analyzes the environment presented to the application. This which enables the user of an application to place a virtual object on the physical environment. The tests were done to see how ARCore works during different light levels, different types of surfaces, different angles and the difference between having the device stationary or moving. From the testing that were done some conclusions could be drawn about the light levels, surfaces and differences between a moving and stationary device. More research and testing following these principles need to be done to draw even more conclusions of the system and its limitations. How these should be done is presented and discussed.

Abstract [sv]

Forstarkt verklighet (augmented reality) har stigit under en tid och börjat ta sig in på mobilmarknaden for både IOS och Android. År 2017 släppte Apple ARKit för IOS vilket är en utvecklingsplattform för att utveckla applikationer inom förstärkt verklighet. Som svar på detta så slappte Google sin egna utvecklingsplattform vid namn ARCore, som släpptes den 1 mars 2018. ARCore är också en utvecklingsplattform för utvecklandet av applikationer inom förstarkt verklighet men istället inom Android, Unity och Unreal. Sedan ARCore släpptes nyligen är det fortfarande okant vilka särskilda begränsningar som kan finnas för det. Syftet med denna avhandling är att ge företag och utvecklare en indikation om ARCores potentiella begränsningar. Målet med denna avhandling och arbete är att kartlägga hur väl ARCore fungerar under olika omstandigheter, och i synnerhet hur dess struktursdetektor fungerar och beter sig.

En kvantitativ forskning gjordes med användning av fallstudie metoden. Olika tester utfördes med en modifierad test-applikation från Google. Testerna inkluderade testning av hur ARCores struktursdetektor, processen som analyserar miljön runt om sig, fungerar. Denna teknik möjliggor att användaren av en applikation kan placera ett virtuellt objekt på den fysiska miljön. Testen innebar att se hur ARCore arbetar under olika ljusnivåer, olika typer av ytor, olika vinklar och skillnaden mellan att ha enheten stationär eller rör på sig. Från testningen som gjordes kan man dra några slutsatser om ljusnivåer, ytor och skillnader mellan en rörlig och stationar enhet. Mer forskning och testning enligt dessa principer måste göras för att dra ännu mer slutsatser av systemet och dess begränsningar. Hur dessa ska göras presenteras och diskuteras.

Place, publisher, year, edition, pages
2018. , p. 58
Series
TRITA-EECS-EX ; 2018:466
Keywords [en]
ARCore; augmented reality; Android; feature detection; markerless tracking; Google
Keywords [sv]
ARCore; förstarkt verklighet; Android; struktursdetektor; markörlös spårning; Google
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-254357OAI: oai:DiVA.org:kth-254357DiVA, id: diva2:1331201
Subject / course
Information and Communication Technology
Educational program
Master of Science in Engineering - Information and Communication Technology
Examiners
Available from: 2019-06-26 Created: 2019-06-26 Last updated: 2019-06-26Bibliographically approved

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