{"@context":{"@vocab":"https://cir.nii.ac.jp/schema/1.0/","rdfs":"http://www.w3.org/2000/01/rdf-schema#","dc":"http://purl.org/dc/elements/1.1/","dcterms":"http://purl.org/dc/terms/","foaf":"http://xmlns.com/foaf/0.1/","prism":"http://prismstandard.org/namespaces/basic/2.0/","cinii":"http://ci.nii.ac.jp/ns/1.0/","datacite":"https://schema.datacite.org/meta/kernel-4/","ndl":"http://ndl.go.jp/dcndl/terms/","jpcoar":"https://github.com/JPCOAR/schema/blob/master/2.0/"},"@id":"https://cir.nii.ac.jp/crid/1360848660077800960.json","@type":"Article","productIdentifier":[{"identifier":{"@type":"DOI","@value":"10.1109/aim.2017.8014186"}},{"identifier":{"@type":"URI","@value":"http://xplorestaging.ieee.org/ielx7/7999201/8013982/08014186.pdf?arnumber=8014186"}}],"resourceType":"学術雑誌論文(journal article)","dc:title":[{"@value":"Laser-based cooperative tracking of vehicles and people by multiple mobile robots in GNSS-denied environments"}],"description":[{"notation":[{"@value":"This paper presents laser-based tracking (estimation of pose and size) of moving objects using multiple mobile robots as sensor nodes in Global navigation satellite system (GNSS)-denied environments. Each sensor node is equipped with a multilayer laser scanner and detects moving objects, such as people, cars, and bicycles, in its own laser-scanned images by applying an occupancy-grid-based method. It then sends measurement information related to the moving objects to a central server. The central server estimates the objects' poses (positions and velocities) and sizes using Bayesian filter. In this cooperative-tracking method, the nearby sensor nodes always share their tracking information, allowing tracking of invisible or partially visible objects. To perform reliable cooperative tracking, sensor nodes should accurately identify their relative pose. In GNSS-denied environments, the relative pose is estimated by scan matching using laser measurements captured by nearby sensor nodes. Such cooperative scan matching is performed by 4-points congruent sets (4PCS) matching method for coarse registration and Iterative closest point (ICP) method for fine registration. The experimental results of tracking a car, a motorcycle, and a pedestrian with two sensor nodes in an outdoor GNSS-denied environment validate the proposed method."}]}],"creator":[{"@id":"https://cir.nii.ac.jp/crid/1380848660077800963","@type":"Researcher","foaf:name":[{"@value":"Shota Kanaki"}]},{"@id":"https://cir.nii.ac.jp/crid/1420564276159151232","@type":"Researcher","personIdentifier":[{"@type":"KAKEN_RESEARCHERS","@value":"10145815"},{"@type":"NRID","@value":"1000010145815"},{"@type":"ORCID","@value":"0000-0003-2274-2366"},{"@type":"AID","@value":"DA19794191"},{"@type":"NRID","@value":"9000415306783"},{"@type":"NRID","@value":"9000404340972"},{"@type":"NRID","@value":"9000415422878"},{"@type":"RESEARCHMAP","@value":"https://researchmap.jp/read0016561"}],"foaf:name":[{"@value":"Masafumi Hashimoto"}]},{"@id":"https://cir.nii.ac.jp/crid/1380848660077800960","@type":"Researcher","foaf:name":[{"@value":"Yuto 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