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Evaluation of Fisheye-Camera Based Visual Multi-Session Localization in a Real-World Scenario
Volkswagen AG.
ETH Zurich.
Volkswagen AG.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).ORCID iD: 0000-0003-3513-8854
2013 (English)In: Intelligent Vehicles Symposium (IV), 2013 IEEE Workshop on Environment Perception and Navigation for Intelligent Vehicles, Piscataway, NJ: IEEE Operations Center , 2013, 57-62 p.Conference paper (Refereed)
Abstract [en]

The European V-Charge project seeks to develop fully automated valet parking and charging of electric vehicles using only low-cost sensors. One of the challenges is to implement robust visual localization using only cameras and stock vehicle sensors. We integrated four monocular, wide-angle, fisheye cameras on a consumer car and implemented a mapping and localization pipeline. Visual features and odometry are combined to build and localize against a keyframe-based three dimensional map. We report results for the first stage of the project, based on two months worth of data acquired under varying conditions, with the objective of localizing against a map created offline. © 2013 IEEE.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Operations Center , 2013. 57-62 p.
, IEEE Intelligent Vehicles Symposium, ISSN 1931-0587
Keyword [en]
Fish-eye cameras, Fully automated, Low-cost sensors, Mapping and localization, Real-world scenario, Three-dimensional maps, Vehicle sensors, Visual localization
National Category
Computer Vision and Robotics (Autonomous Systems)
URN: urn:nbn:se:hh:diva-23382DOI: 10.1109/IVS.2013.6629447ISI: 000333750200010ScopusID: 2-s2.0-84892396299ISBN: 978-1-4673-2754-1OAI: diva2:642048
IEEE Intelligent Vehicles Symposium, June 23-26, 2013, Gold Cost, Australia
Available from: 2013-08-20 Created: 2013-08-20 Last updated: 2015-12-14Bibliographically approved
In thesis
1. Lifelong Visual Localization for Automated Vehicles
Open this publication in new window or tab >>Lifelong Visual Localization for Automated Vehicles
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Automated driving can help solve the current and future problems of individualtransportation. Automated valet parking is a possible approach to help with overcrowded parking areas in cities and make electric vehicles more appealing. In an automated valet system, drivers are able to drop off their vehicle close to a parking area. The vehicle drives to a free parking spot on its own, while the driver is free to perform other tasks — such as switching the mode of transportation. Such a system requires the automated car to navigate unstructured, possibly three dimensional areas. This goes beyond the scope ofthe tasks performed in the state of the art for automated driving.

This thesis describes a visual localization system that provides accuratemetric pose estimates. As sensors, the described system uses multiple monocular cameras and wheel-tick odometry. This is a sensor set-up that is close to what can be found in current production cars. Metric pose estimates with errors in the order of tens of centimeters enable maneuvers such as parking into tight parking spots. This system forms the basis for automated navigationin the EU-funded V-Charge project.

Furthermore, we present an approach to the challenging problem of life-long mapping and localization. Over long time spans, the visual appearance ofthe world is subject to change due to natural and man-made phenomena. The effective long-term usage of visual maps requires the ability to adapt to these changes. We describe a multi-session mapping system, that fuses datasets intoiiia single, unambiguous, metric representation. This enables automated navigation in the presence of environmental change. To handle the growing complexityof such a system we propose the concept of Summary Maps, which contain a reduced set of landmarks that has been selected through a combination of scoring and sampling criteria. We show that a Summary Map with bounded complexity can achieve accurate localization under a wide variety of conditions.

Finally, as a foundation for lifelong mapping, we propose a relational database system. This system is based on use-cases that are not only concerned with solving the basic mapping problem, but also with providing users with a better understanding of the long-term processes that comprise a map. We demonstrate that we can pose interesting queries to the database, that help us gain a better intuition about the correctness and robustness of the created maps. This is accomplished by answering questions about the appearance and distribution of visual landmarks that were used during mapping. This thesis takes on one of the major unsolved challenges in vision-based localization and mapping: long-term operation in a changing environment. We approach this problem through extensive real world experimentation, as well as in-depth evaluation and analysis of recorded data. We demonstrate that accurate metric localization is feasible both during short term changes, as exemplified by the transition between day and night, as well as longer term changes, such as due to seasonal variation.

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2015. 74 p.
Halmstad University Dissertations, 12
vision-based localization, automated vehicles
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
urn:nbn:se:hh:diva-28239 (URN)978-91-87045-27-1 (ISBN)978-91-87045-26-4 (ISBN)
Public defence
2015-05-13, Wigforssalen, Visionen, Kristian IV:s väg 3, 301 18, Halmstad, 13:15 (English)
Available from: 2015-05-12 Created: 2015-05-11 Last updated: 2016-01-08Bibliographically approved

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