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Summary Maps for Lifelong Visual Localization
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
ETH, Zürich, Switzerland.
ETH, Zürich, Switzerland.
Volkswagen AG, Wolfsburg, Germany.
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2016 (English)In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 33, no 5, 561-590 p.Article in journal (Refereed) Published
Abstract [en]

Robots that use vision for localization need to handle environments which are subject to seasonal and structural change, and operate under changing lighting and weather conditions. We present a framework for lifelong localization and mapping designed to provide robust and metrically accurate online localization in these kinds of changing environments. Our system iterates between offline map building, map summary, and online localization. The offline mapping fuses data from multiple visually varied datasets, thus dealing with changing environments by incorporating new information. Before passing this data to the online localization system, the map is summarized, selecting only the landmarks that are deemed useful for localization. This Summary Map enables online localization that is accurate and robust to the variation of visual information in natural environments while still being computationally efficient.

We present a number of summary policies for selecting useful features for localization from the multi-session map and explore the tradeoff between localization performance and computational complexity. The system is evaluated on 77 recordings, with a total length of 30 kilometers, collected outdoors over sixteen months. These datasets cover all seasons, various times of day, and changing weather such as sunshine, rain, fog, and snow. We show that it is possible to build consistent maps that span data collected over an entire year, and cover day-to-night transitions. Simple statistics computed on landmark observations are enough to produce a Summary Map that enables robust and accurate localization over a wide range of seasonal, lighting, and weather conditions. © 2015 Wiley Periodicals, Inc.

Place, publisher, year, edition, pages
Hoboken, NJ: John Wiley & Sons, 2016. Vol. 33, no 5, 561-590 p.
Keyword [en]
Field robotics
National Category
Robotics
Identifiers
URN: urn:nbn:se:hh:diva-27978DOI: 10.1002/rob.21595Scopus ID: 2-s2.0-84931069651OAI: oai:DiVA.org:hh-27978DiVA: diva2:794800
Funder
EU, FP7, Seventh Framework Programme, 269916EU, FP7, Seventh Framework Programme, 610603
Note

This work is supported in part by the European Community’s Seventh Framework Programme (FP7/2007-2013) under Grants No. 269916 (V-Charge) and No. 610603 (EUROPA2).

Available from: 2015-03-12 Created: 2015-03-12 Last updated: 2016-07-22Bibliographically 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.
Series
Halmstad University Dissertations, 12
Keyword
vision-based localization, automated vehicles
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
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)
Opponent
Supervisors
Available from: 2015-05-12 Created: 2015-05-11 Last updated: 2016-01-08Bibliographically approved

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Citation style
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