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Two Camera System for Robot Applications; Navigation
Mälardalen University, School of Innovation, Design and Engineering.
Mälardalen University, School of Innovation, Design and Engineering.ORCID iD: 0000-0001-7934-6917
Mälardalen University, School of Innovation, Design and Engineering.ORCID iD: 0000-0001-5141-7242
2008 (English)In: 13th IEEE International Conference on Emerging Technologies and Factory Automation, 2008. (ETFA 2008), 2008, p. 345-352Conference paper, Published paper (Refereed)
Abstract [en]

Current approaches to feature detection and matching

in images strive to increase the repeatability of the detector

and minimize the degree of outliers in the matching.

In this paper we present a conflicting approach; we suggest

that a lower performance feature detector can produce

a result more than adequate for robot navigation irrespectively

of the amount of outliers. By using an FPGA

together with two cameras we can remove the need for

descriptors by performing what we call spurious matching

and the use of 3D landmarks. The approach bypasses

the problem of outliers and reduces the time consuming

task of data association, which slows many matching algorithms.

Place, publisher, year, edition, pages
2008. p. 345-352
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-7254DOI: 10.1109/ETFA.2008.4638417Scopus ID: 2-s2.0-56349088961ISBN: 1424415063 (print)OAI: oai:DiVA.org:mdh-7254DiVA, id: diva2:237264
Conference
13th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2008; Hamburg; Germany; 15 September 2008 through 18 September 2008
Available from: 2009-09-25 Created: 2009-09-25 Last updated: 2014-01-10Bibliographically approved
In thesis
1. Stereo vision algorithms in reconfigurable hardware for robotics applications
Open this publication in new window or tab >>Stereo vision algorithms in reconfigurable hardware for robotics applications
2011 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis presents image processing solutions in FPGA based embedded vision systems. Image processing is a demanding process but the information that can be extracted from images is very useful and can be used for many tasks like mapping and navigation, object detection and recognition, collision detection and more.

Image processing or analysis involves reading images from a camera system, improve an image with respect to colour fidelity and white balance, removing distortion, extracting salient information. The mentioned steps are often referred to as low to medium level image processing and involve large amounts of data and fairly simple algorithms suitable for parallel processing.

Medium to high level processing involves a reduced amount of data and more complex algorithms. Object recognition which involves matching image features to information stored in a database is of higher complexity.

A vision system can be used in anything from a car to industry processes to mobile robots playing soccer or assisting people in their homes. A vision system often works with video streams that are processed to find pieces that can be handled in an industry process, detect obstacles that may be potential hazards in traffic or to find and track landmarks in the environment that can be used to build and navigate from. This involves large amount of calculations and this is a problem, even though modern computers are fast they may still not be able to execute the desired algorithms with the frequency wanted. Even if there are computers that are fast enough they are bulky and require a lot of power. They are not suitable for incorporating on small mobile robots.

In this thesis I will present the image processing sequence to give an understanding of the complexity of the processes involved and I will discuss some processing platforms suitable for image processing. I will also present my work that is focused on image algorithm implementations for reconfigurable hardware suitable for mobile robots with requirements on speed an power consumption.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2011
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 141
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-12977 (URN)978-91-7485-033-8 (ISBN)
Presentation
2011-09-26, Lambda, 09:15 (English)
Opponent
Supervisors
Available from: 2011-09-08 Created: 2011-09-08 Last updated: 2018-01-12Bibliographically approved
2. Resource Optimized Stereo Matching in Reconfigurable Hardware for Autonomous Systems
Open this publication in new window or tab >>Resource Optimized Stereo Matching in Reconfigurable Hardware for Autonomous Systems
2011 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

There is a need for compact, high-speed, and low-power vision systems for enabling real-time mobile autonomous applications. The best approach to achieve this is to implement the bulk of the application in hardware. Reconfigurable hardware meet these requirements without the limitation of fixed functionality that accompanies application-specific circuits. Resource constraints of reconfigurable hardware calls for optimized implementations i terms of resource usage with maintained performance.

The research group in Robotics at Mälardalen University is moving toward the completion of a reconfigurable hardware-platform for stereo vision, coupled with a compact embedded computer. This system will incorporate hardware-based preprocessing components enabling visual perception for autonomous machines. This thesis covers the reconfigurable hardware section of the vision system concerning the realization of scene depth extraction. It shows the advantages of image preprocessing in hardware and propose a resource optimized approach to stereo matching. The work quantifies the impact of reduced resource utilization and a desire for increased accuracy in disparity estimation. The implemented stereo matching approach performs on par with recent similar implementations in terms of accuracy, but excels in terms of resource utilization and resource sharing, as the external memory requirement is removed for larger images.

Future work aims to further include processes for navigation, and structure and object recognition. Furthermore, the system will be adapted to real world scenarios, both indoors and outdoors.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2011
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 142
National Category
Robotics Embedded Systems
Research subject
Electronics
Identifiers
urn:nbn:se:mdh:diva-12996 (URN)978-91-7485-035-2 (ISBN)
Presentation
2011-09-26, Beta, 14:00 (English)
Opponent
Supervisors
Available from: 2011-09-14 Created: 2011-09-14 Last updated: 2013-12-03Bibliographically approved

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Ekstrand, FredrikAsplund, Lars

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