Difference between revisions of "Publications:Summary Maps for Lifelong Visual Localization"
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{{PublicationSetupTemplate|Author=Peter Mühlfellner, Mathias Bürki, Mike Bosse, Wojciech Derendarz, Roland Philippsen, Paul Furgale | {{PublicationSetupTemplate|Author=Peter Mühlfellner, Mathias Bürki, Mike Bosse, Wojciech Derendarz, Roland Philippsen, Paul Furgale | ||
|PID=794800 | |PID=794800 | ||
| − | |Name=Mühlfellner, Peter (Högskolan i Halmstad [2804], Akademin för informationsteknologi [16904], Halmstad Embedded and Intelligent Systems Research (EIS) [3938], CAISR Centrum för tillämpade intelligenta system (IS-lab) [13650]);Bürki, Mathias (ETH, Zürich, Switzerland);Bosse, Mike (ETH, Zürich, Switzerland);Derendarz, Wojciech (Volkswagen AG);Philippsen, Roland [rolphi] [0000-0003-3513-8854] (Högskolan i Halmstad [2804], Akademin för informationsteknologi [16904], Halmstad Embedded and Intelligent Systems Research (EIS) [3938], CAISR Centrum för tillämpade intelligenta system (IS-lab) [13650]);Furgale, Paul (ETH, Zürich, Switzerland) | + | |Name=Mühlfellner, Peter (Högskolan i Halmstad [2804], Akademin för informationsteknologi [16904], Halmstad Embedded and Intelligent Systems Research (EIS) [3938], CAISR Centrum för tillämpade intelligenta system (IS-lab) [13650]);Bürki, Mathias (ETH, Zürich, Switzerland);Bosse, Mike (ETH, Zürich, Switzerland);Derendarz, Wojciech (Volkswagen AG, Wolfsburg, Germany);Philippsen, Roland [rolphi] [0000-0003-3513-8854] (Högskolan i Halmstad [2804], Akademin för informationsteknologi [16904], Halmstad Embedded and Intelligent Systems Research (EIS) [3938], CAISR Centrum för tillämpade intelligenta system (IS-lab) [13650]);Furgale, Paul (ETH, Zürich, Switzerland) |
|Title=Summary Maps for Lifelong Visual Localization | |Title=Summary Maps for Lifelong Visual Localization | ||
|PublicationType=Journal Paper | |PublicationType=Journal Paper | ||
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|Journal=Journal of Field Robotics | |Journal=Journal of Field Robotics | ||
|JournalISSN=1556-4959 | |JournalISSN=1556-4959 | ||
| − | |Status= | + | |Status=published |
| − | |Volume= | + | |Volume=33 |
| − | |Issue= | + | |Issue=5 |
|HostPublication= | |HostPublication= | ||
|Conference= | |Conference= | ||
| − | |StartPage= | + | |StartPage=561 |
| − | |EndPage= | + | |EndPage=590 |
| − | |Year= | + | |Year=2016 |
|Edition= | |Edition= | ||
|Pages= | |Pages= | ||
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|Urls= | |Urls= | ||
|ISRN= | |ISRN= | ||
| − | |DOI= | + | |DOI=http://dx.doi.org/10.1002/rob.21595 |
|ISI= | |ISI= | ||
|PMID= | |PMID= | ||
| − | |ScopusId= | + | |ScopusId=2-s2.0-84931069651 |
|NBN=urn:nbn:se:hh:diva-27978 | |NBN=urn:nbn:se:hh:diva-27978 | ||
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|Projects= | |Projects= | ||
| − | |Notes= | + | |Notes=<p>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).</p> |
| − | |Abstract=<p>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.</p><p>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.</p> | + | |Abstract=<p>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.</p><p>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.</p> |
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|CreatedDate=2015-03-12 | |CreatedDate=2015-03-12 | ||
|PublicationDate=2015-03-12 | |PublicationDate=2015-03-12 | ||
| − | |LastUpdated= | + | |LastUpdated=2016-07-22 |
|diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:794800}} | |diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:794800}} | ||
<div style='display: none'> | <div style='display: none'> | ||
Revision as of 20:46, 30 September 2016
| Title | Summary Maps for Lifelong Visual Localization |
|---|---|
| Author | Peter Mühlfellner and Mathias Bürki and Mike Bosse and Wojciech Derendarz and Roland Philippsen and Paul Furgale |
| Year | 2016 |
| PublicationType | Journal Paper |
| Journal | Journal of Field Robotics |
| HostPublication | |
| Conference | |
| DOI | http://dx.doi.org/10.1002/rob.21595 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:794800 |
| Abstract | 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. |
