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Closing the Loop: Mobile Visual Location Recognition
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Visual simultaneous localization and mapping (SLAM) as field has been researched for ten years, but with recent advances in mobile performance visual SLAM is entering the consumer market in a completely new way. A visual SLAM system will however be sensitive to non cautious use that may result in severe motion, occlusion or poor surroundings in terms of visual features that will cause the system to temporarily fail. The procedure of recovering from such a fail is called relocalization. Together with two similar problems localization, to find your position in an existing SLAM session, and loop closing, the online reparation and perfection of the map in an active SLAM session, these can be grouped as visual location recognition (VLR).

This thesis presents novel results by combining the scalability of FabMap and the precision of 13th Lab's tracking yielding high-precision VLR, +/- 10 cm, while maintaining above 99 % precision and 60 % recall for sessions containing thousands of images. Everything functional purely on a normal mobile phone.

The applications of VLR are many. Indoors, where GPS is not functioning, VLR can still provide positional information and navigate you through big complexes like airports and museums. Outdoors, VLR can improve the precision of GPS tenfold yielding a new level of navigational experience. Virtual and augmented reality applications are other areas that benefit from improved positioning and localization.

Place, publisher, year, edition, pages
2014. , 73 p.
Keyword [en]
Visual Location Recognition, Localization, Relocalization, Loop Closing, SLAM, FabMap, Direct Matching, Computer Vision
National Category
Computer Vision and Robotics (Autonomous Systems) Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-112547ISRN: LiTH-ISY-EX--14/4813--SEOAI: oai:DiVA.org:liu-112547DiVA: diva2:767444
External cooperation
13th Lab
Subject / course
Computer Vision Laboratory
Supervisors
Examiners
Available from: 2014-12-01 Created: 2014-12-01 Last updated: 2014-12-02Bibliographically approved

Open Access in DiVA

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