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The Auto-Complete Graph: Merging and Mutual Correction of Sensor and Prior Maps for SLAM
Örebro University, School of Science and Technology.ORCID iD: 0000-0002-3079-0512
Örebro University, School of Science and Technology.ORCID iD: 0000-0001-8658-2985
Örebro University, School of Science and Technology.ORCID iD: 0000-0003-0217-9326
2019 (English)In: Robotics, E-ISSN 2218-6581, Vol. 8, no 2, article id 40Article in journal (Refereed) Published
Abstract [en]

Simultaneous Localization And Mapping (SLAM) usually assumes the robot starts without knowledge of the environment. While prior information, such as emergency maps or layout maps, is often available, integration is not trivial since such maps are often out of date and have uncertainty in local scale. Integration of prior map information is further complicated by sensor noise, drift in the measurements, and incorrect scan registrations in the sensor map. We present the Auto-Complete Graph (ACG), a graph-based SLAM method merging elements of sensor and prior maps into one consistent representation. After optimizing the ACG, the sensor map's errors are corrected thanks to the prior map, while the sensor map corrects the local scale inaccuracies in the prior map. We provide three datasets with associated prior maps: two recorded in campus environments, and one from a fireman training facility. Our method handled up to 40% of noise in odometry, was robust to varying levels of details between the prior and the sensor map, and could correct local scale errors of the prior. In field tests with ACG, users indicated points of interest directly on the prior before exploration. We did not record failures in reaching them.

Place, publisher, year, edition, pages
MDPI , 2019. Vol. 8, no 2, article id 40
Keywords [en]
SLAM, prior map, emergency map, layout map, graph-based SLAM, navigation, search and rescue
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:oru:diva-75742DOI: 10.3390/robotics8020040ISI: 000475325600017Scopus ID: 2-s2.0-85069926702OAI: oai:DiVA.org:oru-75742DiVA, id: diva2:1342185
Funder
Knowledge Foundation, 20140220
Note

Funding Agency:

EU  ICT-26-2016 732737  ICT-23-2014 645101

Available from: 2019-08-13 Created: 2019-08-13 Last updated: 2019-08-13Bibliographically approved

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Mielle, MalcolmMagnusson, MartinLilienthal, Achim

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  • apa
  • harvard1
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Output format
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