Consistent map building in petrochemical complexes for firefighter robots using SLAM based on GPS and LIDAR View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Abu Ubaidah Shamsudin, Kazunori Ohno, Ryunosuke Hamada, Shotaro Kojima, Thomas Westfechtel, Takahiro Suzuki, Yoshito Okada, Satoshi Tadokoro, Jun Fujita, Hisanori Amano

ABSTRACT

The objective of this study was to achieve simultaneous localization and mapping (SLAM) of firefighter robots for petrochemical complexes. Consistency of the SLAM map is important because human operators compare the map with aerial images and identify target positions on the map. The global positioning system (GPS) enables increased consistency. Therefore, this paper describes two Rao-Blackwellized particle filters (RBPFs) based on GPS and light detection and ranging (LIDAR) as SLAM solutions. Fast-SLAM 1.0 and Fast-SLAM 2.0 were used in grid maps for RBPFs in this study. We herein propose the use of Fast-SLAM to combine GPS and LIDAR. The difference between the original Fast-SLAM and the proposed method is the use of the log-likelihood function of GPS; the proposed combination method is implemented using a probabilistic mathematics formulation. The proposed methods were evaluated using sensor data measured in a real petrochemical complex in Japan ranging in size from 550–380 m. RTK-GPS data was used for the GPS measurement and had an availability of 56%. Our results showed that Fast-SLAM 2.0 based on GPS and LIDAR in a dense grid map produced the best results. There was significant improvement in alignment to aerial data, and the mean square root error was 0.65 m. To evaluate the mapping consistency, accurate 3D point cloud data measured by Faro Focus 3D (± 3 mm) was used as the ground truth. Building sizes were compared; the minimum mean errors were 0.17 and 0.08 m for the oil refinery and management building area and the area of a sparse building layout with large oil tanks, respectively. Consequently, a consistent map, which was also consistent with an aerial map (from Google Maps), was built by Fast-SLAM 1.0 and 2.0 based on GPS and LIDAR. Our method reproduced map consistency results for ten runs with a variance of ± 0.3 m. Our method reproduced map consistency results with a global accuracy of 0.52 m in a low RTK-Fix-GPS environment, which was a factory with a building layout similar to petrochemical complexes with 20.9% of RTK-Fix-GPS data availability. More... »

PAGES

7

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s40648-018-0104-z

DOI

http://dx.doi.org/10.1186/s40648-018-0104-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1103152114


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0909", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Geomatic Engineering", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/09", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Engineering", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Tohoku University", 
          "id": "https://www.grid.ac/institutes/grid.69566.3a", 
          "name": [
            "Graduate School of Information Sciences, Tohoku University, 6-6-01 Aramaki Aza Aoba, Aoba-ku, 980-8579, Sendai, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Shamsudin", 
        "givenName": "Abu Ubaidah", 
        "id": "sg:person.07605467735.61", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07605467735.61"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tohoku University", 
          "id": "https://www.grid.ac/institutes/grid.69566.3a", 
          "name": [
            "New Industry Creation Hatchery Center, Tohoku University, 6-6-10, Aza-Aoba, Aramaki, Aoba-ku, 980-8579, Sendai, Miyagi, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ohno", 
        "givenName": "Kazunori", 
        "id": "sg:person.012215060513.07", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012215060513.07"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tohoku University", 
          "id": "https://www.grid.ac/institutes/grid.69566.3a", 
          "name": [
            "New Industry Creation Hatchery Center, Tohoku University, 6-6-10, Aza-Aoba, Aramaki, Aoba-ku, 980-8579, Sendai, Miyagi, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hamada", 
        "givenName": "Ryunosuke", 
        "id": "sg:person.015132144177.16", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015132144177.16"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tohoku University", 
          "id": "https://www.grid.ac/institutes/grid.69566.3a", 
          "name": [
            "Graduate School of Information Sciences, Tohoku University, 6-6-01 Aramaki Aza Aoba, Aoba-ku, 980-8579, Sendai, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kojima", 
        "givenName": "Shotaro", 
        "id": "sg:person.015064030777.07", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015064030777.07"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tohoku University", 
          "id": "https://www.grid.ac/institutes/grid.69566.3a", 
          "name": [
            "Graduate School of Information Sciences, Tohoku University, 6-6-01 Aramaki Aza Aoba, Aoba-ku, 980-8579, Sendai, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Westfechtel", 
        "givenName": "Thomas", 
        "id": "sg:person.015675707165.98", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015675707165.98"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tohoku University", 
          "id": "https://www.grid.ac/institutes/grid.69566.3a", 
          "name": [
            "New Industry Creation Hatchery Center, Tohoku University, 6-6-10, Aza-Aoba, Aramaki, Aoba-ku, 980-8579, Sendai, Miyagi, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Suzuki", 
        "givenName": "Takahiro", 
        "id": "sg:person.012017720422.29", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012017720422.29"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tohoku University", 
          "id": "https://www.grid.ac/institutes/grid.69566.3a", 
          "name": [
            "Graduate School of Information Sciences, Tohoku University, 6-6-01 Aramaki Aza Aoba, Aoba-ku, 980-8579, Sendai, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Okada", 
        "givenName": "Yoshito", 
        "id": "sg:person.016005611471.23", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016005611471.23"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tohoku University", 
          "id": "https://www.grid.ac/institutes/grid.69566.3a", 
          "name": [
            "Graduate School of Information Sciences, Tohoku University, 6-6-01 Aramaki Aza Aoba, Aoba-ku, 980-8579, Sendai, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tadokoro", 
        "givenName": "Satoshi", 
        "id": "sg:person.013454033251.77", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013454033251.77"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Mitsubishi Heavy Industries (Japan)", 
          "id": "https://www.grid.ac/institutes/grid.471153.5", 
          "name": [
            "Equipment Designing Section, Nuclear Plant Component Designing Department, Mitsubishi Heavy Industries LTD., 1-1,Wadasaki-Cho 1-Chome, Hyogo-Ku, 652-8585, Kobe, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Fujita", 
        "givenName": "Jun", 
        "id": "sg:person.012645233021.58", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012645233021.58"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Research Institute of Fire and Disaster", 
          "id": "https://www.grid.ac/institutes/grid.471863.f", 
          "name": [
            "National Research Institute of Fire and Disaster, Fire and Disaster Management Agency JAPAN, 4-35-3, Jindaiji-Higashimachi, 182-8508, Chofu, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Amano", 
        "givenName": "Hisanori", 
        "id": "sg:person.016537210717.85", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016537210717.85"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1177/0278364906072768", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002074245"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/0278364906072768", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002074245"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1117/12.849593", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008075677"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3390/s111110197", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011109505"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10514-012-9321-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023420089", 
          "https://doi.org/10.1007/s10514-012-9321-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3390/s130100119", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023838198"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3390/s16122201", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024457690"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/bf03351948", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024598224", 
          "https://doi.org/10.1186/bf03351948"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4757-3437-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039895902", 
          "https://doi.org/10.1007/978-1-4757-3437-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4757-3437-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039895902", 
          "https://doi.org/10.1007/978-1-4757-3437-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.4236/iim.2010.27051", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050438900"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/78.978396", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061231815"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/mra.2006.1638022", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061419410"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/mra.2006.1678144", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061419427"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tim.2013.2265476", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061639555"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tro.2006.889486", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061784717"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tro.2008.2006706", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061784890"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iros.2008.4650585", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093922006"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/plans.2008.4570059", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094451790"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/robot.2010.5509636", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094525997"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iros.2010.5649043", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095080302"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/robot.2009.5152370", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095197834"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-12", 
    "datePublishedReg": "2018-12-01", 
    "description": "The objective of this study was to achieve simultaneous localization and mapping (SLAM) of firefighter robots for petrochemical complexes. Consistency of the SLAM map is important because human operators compare the map with aerial images and identify target positions on the map. The global positioning system (GPS) enables increased consistency. Therefore, this paper describes two Rao-Blackwellized particle filters (RBPFs) based on GPS and light detection and ranging (LIDAR) as SLAM solutions. Fast-SLAM 1.0 and Fast-SLAM 2.0 were used in grid maps for RBPFs in this study. We herein propose the use of Fast-SLAM to combine GPS and LIDAR. The difference between the original Fast-SLAM and the proposed method is the use of the log-likelihood function of GPS; the proposed combination method is implemented using a probabilistic mathematics formulation. The proposed methods were evaluated using sensor data measured in a real petrochemical complex in Japan ranging in size from 550\u2013380 m. RTK-GPS data was used for the GPS measurement and had an availability of 56%. Our results showed that Fast-SLAM 2.0 based on GPS and LIDAR in a dense grid map produced the best results. There was significant improvement in alignment to aerial data, and the mean square root error was 0.65 m. To evaluate the mapping consistency, accurate 3D point cloud data measured by Faro Focus 3D (\u00b1 3 mm) was used as the ground truth. Building sizes were compared; the minimum mean errors were 0.17 and 0.08 m for the oil refinery and management building area and the area of a sparse building layout with large oil tanks, respectively. Consequently, a consistent map, which was also consistent with an aerial map (from Google Maps), was built by Fast-SLAM 1.0 and 2.0 based on GPS and LIDAR. Our method reproduced map consistency results for ten runs with a variance of \u00b1 0.3 m. Our method reproduced map consistency results with a global accuracy of 0.52 m in a low RTK-Fix-GPS environment, which was a factory with a building layout similar to petrochemical complexes with 20.9% of RTK-Fix-GPS data availability.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/s40648-018-0104-z", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1135873", 
        "issn": [
          "2197-4225"
        ], 
        "name": "ROBOMECH Journal", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "5"
      }
    ], 
    "name": "Consistent map building in petrochemical complexes for firefighter robots using SLAM based on GPS and LIDAR", 
    "pagination": "7", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s40648-018-0104-z"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "7f12e7855df27ace27ccf4526231eb37dde46368ad9c957f9035f8722e39f3c2"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1103152114"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s40648-018-0104-z", 
      "https://app.dimensions.ai/details/publication/pub.1103152114"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-15T09:16", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000376_0000000376/records_56171_00000004.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1186%2Fs40648-018-0104-z"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/s40648-018-0104-z'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s40648-018-0104-z'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s40648-018-0104-z'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s40648-018-0104-z'


 

This table displays all metadata directly associated to this object as RDF triples.

193 TRIPLES      21 PREDICATES      47 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s40648-018-0104-z schema:about anzsrc-for:09
2 anzsrc-for:0909
3 schema:author Ncf1147d458f44f0fa20f6f7b18003cf9
4 schema:citation sg:pub.10.1007/978-1-4757-3437-9
5 sg:pub.10.1007/s10514-012-9321-0
6 sg:pub.10.1186/bf03351948
7 https://doi.org/10.1109/78.978396
8 https://doi.org/10.1109/iros.2008.4650585
9 https://doi.org/10.1109/iros.2010.5649043
10 https://doi.org/10.1109/mra.2006.1638022
11 https://doi.org/10.1109/mra.2006.1678144
12 https://doi.org/10.1109/plans.2008.4570059
13 https://doi.org/10.1109/robot.2009.5152370
14 https://doi.org/10.1109/robot.2010.5509636
15 https://doi.org/10.1109/tim.2013.2265476
16 https://doi.org/10.1109/tro.2006.889486
17 https://doi.org/10.1109/tro.2008.2006706
18 https://doi.org/10.1117/12.849593
19 https://doi.org/10.1177/0278364906072768
20 https://doi.org/10.3390/s111110197
21 https://doi.org/10.3390/s130100119
22 https://doi.org/10.3390/s16122201
23 https://doi.org/10.4236/iim.2010.27051
24 schema:datePublished 2018-12
25 schema:datePublishedReg 2018-12-01
26 schema:description The objective of this study was to achieve simultaneous localization and mapping (SLAM) of firefighter robots for petrochemical complexes. Consistency of the SLAM map is important because human operators compare the map with aerial images and identify target positions on the map. The global positioning system (GPS) enables increased consistency. Therefore, this paper describes two Rao-Blackwellized particle filters (RBPFs) based on GPS and light detection and ranging (LIDAR) as SLAM solutions. Fast-SLAM 1.0 and Fast-SLAM 2.0 were used in grid maps for RBPFs in this study. We herein propose the use of Fast-SLAM to combine GPS and LIDAR. The difference between the original Fast-SLAM and the proposed method is the use of the log-likelihood function of GPS; the proposed combination method is implemented using a probabilistic mathematics formulation. The proposed methods were evaluated using sensor data measured in a real petrochemical complex in Japan ranging in size from 550–380 m. RTK-GPS data was used for the GPS measurement and had an availability of 56%. Our results showed that Fast-SLAM 2.0 based on GPS and LIDAR in a dense grid map produced the best results. There was significant improvement in alignment to aerial data, and the mean square root error was 0.65 m. To evaluate the mapping consistency, accurate 3D point cloud data measured by Faro Focus 3D (± 3 mm) was used as the ground truth. Building sizes were compared; the minimum mean errors were 0.17 and 0.08 m for the oil refinery and management building area and the area of a sparse building layout with large oil tanks, respectively. Consequently, a consistent map, which was also consistent with an aerial map (from Google Maps), was built by Fast-SLAM 1.0 and 2.0 based on GPS and LIDAR. Our method reproduced map consistency results for ten runs with a variance of ± 0.3 m. Our method reproduced map consistency results with a global accuracy of 0.52 m in a low RTK-Fix-GPS environment, which was a factory with a building layout similar to petrochemical complexes with 20.9% of RTK-Fix-GPS data availability.
27 schema:genre research_article
28 schema:inLanguage en
29 schema:isAccessibleForFree true
30 schema:isPartOf N4adbc70964374afba246ee205d9de8d3
31 Nfcc82f70c703461a81d7155b7029dda0
32 sg:journal.1135873
33 schema:name Consistent map building in petrochemical complexes for firefighter robots using SLAM based on GPS and LIDAR
34 schema:pagination 7
35 schema:productId N14f37e75aef9412086f2e255e942d33d
36 N913fffb6ce794ad7af2e968d112113cc
37 Ne87dca46daf9416fb2ae8ee637b58b8b
38 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103152114
39 https://doi.org/10.1186/s40648-018-0104-z
40 schema:sdDatePublished 2019-04-15T09:16
41 schema:sdLicense https://scigraph.springernature.com/explorer/license/
42 schema:sdPublisher N5c8a5eae857e499ab50e95895e5e4031
43 schema:url https://link.springer.com/10.1186%2Fs40648-018-0104-z
44 sgo:license sg:explorer/license/
45 sgo:sdDataset articles
46 rdf:type schema:ScholarlyArticle
47 N14f37e75aef9412086f2e255e942d33d schema:name doi
48 schema:value 10.1186/s40648-018-0104-z
49 rdf:type schema:PropertyValue
50 N2c812ab3336744e6bef13de0b63d3e2c rdf:first sg:person.016005611471.23
51 rdf:rest Nf3b2ea8acb7344449cf44cb9cb5daed8
52 N4936dccf99c347229759d52c16b9ba47 rdf:first sg:person.015132144177.16
53 rdf:rest Nc1ee207dcc704f7e99751ed143c9bc48
54 N4adbc70964374afba246ee205d9de8d3 schema:volumeNumber 5
55 rdf:type schema:PublicationVolume
56 N5c8a5eae857e499ab50e95895e5e4031 schema:name Springer Nature - SN SciGraph project
57 rdf:type schema:Organization
58 N6bdd4ea120ca4b9ca991db7ef2a35517 rdf:first sg:person.012017720422.29
59 rdf:rest N2c812ab3336744e6bef13de0b63d3e2c
60 N8f313e908ec24bb4a158c13588e2f721 rdf:first sg:person.012215060513.07
61 rdf:rest N4936dccf99c347229759d52c16b9ba47
62 N913fffb6ce794ad7af2e968d112113cc schema:name readcube_id
63 schema:value 7f12e7855df27ace27ccf4526231eb37dde46368ad9c957f9035f8722e39f3c2
64 rdf:type schema:PropertyValue
65 Nb278f077f09f49d5a3b00274c3079f41 rdf:first sg:person.016537210717.85
66 rdf:rest rdf:nil
67 Nc1ee207dcc704f7e99751ed143c9bc48 rdf:first sg:person.015064030777.07
68 rdf:rest Nd06d878e36ca44898fe93c464ad42ddf
69 Ncf1147d458f44f0fa20f6f7b18003cf9 rdf:first sg:person.07605467735.61
70 rdf:rest N8f313e908ec24bb4a158c13588e2f721
71 Nd06d878e36ca44898fe93c464ad42ddf rdf:first sg:person.015675707165.98
72 rdf:rest N6bdd4ea120ca4b9ca991db7ef2a35517
73 Nd35a9286c019430bb5bbee4b87430a7f rdf:first sg:person.012645233021.58
74 rdf:rest Nb278f077f09f49d5a3b00274c3079f41
75 Ne87dca46daf9416fb2ae8ee637b58b8b schema:name dimensions_id
76 schema:value pub.1103152114
77 rdf:type schema:PropertyValue
78 Nf3b2ea8acb7344449cf44cb9cb5daed8 rdf:first sg:person.013454033251.77
79 rdf:rest Nd35a9286c019430bb5bbee4b87430a7f
80 Nfcc82f70c703461a81d7155b7029dda0 schema:issueNumber 1
81 rdf:type schema:PublicationIssue
82 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
83 schema:name Engineering
84 rdf:type schema:DefinedTerm
85 anzsrc-for:0909 schema:inDefinedTermSet anzsrc-for:
86 schema:name Geomatic Engineering
87 rdf:type schema:DefinedTerm
88 sg:journal.1135873 schema:issn 2197-4225
89 schema:name ROBOMECH Journal
90 rdf:type schema:Periodical
91 sg:person.012017720422.29 schema:affiliation https://www.grid.ac/institutes/grid.69566.3a
92 schema:familyName Suzuki
93 schema:givenName Takahiro
94 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012017720422.29
95 rdf:type schema:Person
96 sg:person.012215060513.07 schema:affiliation https://www.grid.ac/institutes/grid.69566.3a
97 schema:familyName Ohno
98 schema:givenName Kazunori
99 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012215060513.07
100 rdf:type schema:Person
101 sg:person.012645233021.58 schema:affiliation https://www.grid.ac/institutes/grid.471153.5
102 schema:familyName Fujita
103 schema:givenName Jun
104 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012645233021.58
105 rdf:type schema:Person
106 sg:person.013454033251.77 schema:affiliation https://www.grid.ac/institutes/grid.69566.3a
107 schema:familyName Tadokoro
108 schema:givenName Satoshi
109 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013454033251.77
110 rdf:type schema:Person
111 sg:person.015064030777.07 schema:affiliation https://www.grid.ac/institutes/grid.69566.3a
112 schema:familyName Kojima
113 schema:givenName Shotaro
114 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015064030777.07
115 rdf:type schema:Person
116 sg:person.015132144177.16 schema:affiliation https://www.grid.ac/institutes/grid.69566.3a
117 schema:familyName Hamada
118 schema:givenName Ryunosuke
119 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015132144177.16
120 rdf:type schema:Person
121 sg:person.015675707165.98 schema:affiliation https://www.grid.ac/institutes/grid.69566.3a
122 schema:familyName Westfechtel
123 schema:givenName Thomas
124 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015675707165.98
125 rdf:type schema:Person
126 sg:person.016005611471.23 schema:affiliation https://www.grid.ac/institutes/grid.69566.3a
127 schema:familyName Okada
128 schema:givenName Yoshito
129 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016005611471.23
130 rdf:type schema:Person
131 sg:person.016537210717.85 schema:affiliation https://www.grid.ac/institutes/grid.471863.f
132 schema:familyName Amano
133 schema:givenName Hisanori
134 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016537210717.85
135 rdf:type schema:Person
136 sg:person.07605467735.61 schema:affiliation https://www.grid.ac/institutes/grid.69566.3a
137 schema:familyName Shamsudin
138 schema:givenName Abu Ubaidah
139 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07605467735.61
140 rdf:type schema:Person
141 sg:pub.10.1007/978-1-4757-3437-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039895902
142 https://doi.org/10.1007/978-1-4757-3437-9
143 rdf:type schema:CreativeWork
144 sg:pub.10.1007/s10514-012-9321-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023420089
145 https://doi.org/10.1007/s10514-012-9321-0
146 rdf:type schema:CreativeWork
147 sg:pub.10.1186/bf03351948 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024598224
148 https://doi.org/10.1186/bf03351948
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1109/78.978396 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061231815
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1109/iros.2008.4650585 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093922006
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1109/iros.2010.5649043 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095080302
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1109/mra.2006.1638022 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061419410
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1109/mra.2006.1678144 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061419427
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1109/plans.2008.4570059 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094451790
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1109/robot.2009.5152370 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095197834
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1109/robot.2010.5509636 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094525997
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1109/tim.2013.2265476 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061639555
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1109/tro.2006.889486 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061784717
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1109/tro.2008.2006706 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061784890
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1117/12.849593 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008075677
173 rdf:type schema:CreativeWork
174 https://doi.org/10.1177/0278364906072768 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002074245
175 rdf:type schema:CreativeWork
176 https://doi.org/10.3390/s111110197 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011109505
177 rdf:type schema:CreativeWork
178 https://doi.org/10.3390/s130100119 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023838198
179 rdf:type schema:CreativeWork
180 https://doi.org/10.3390/s16122201 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024457690
181 rdf:type schema:CreativeWork
182 https://doi.org/10.4236/iim.2010.27051 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050438900
183 rdf:type schema:CreativeWork
184 https://www.grid.ac/institutes/grid.471153.5 schema:alternateName Mitsubishi Heavy Industries (Japan)
185 schema:name Equipment Designing Section, Nuclear Plant Component Designing Department, Mitsubishi Heavy Industries LTD., 1-1,Wadasaki-Cho 1-Chome, Hyogo-Ku, 652-8585, Kobe, Japan
186 rdf:type schema:Organization
187 https://www.grid.ac/institutes/grid.471863.f schema:alternateName National Research Institute of Fire and Disaster
188 schema:name National Research Institute of Fire and Disaster, Fire and Disaster Management Agency JAPAN, 4-35-3, Jindaiji-Higashimachi, 182-8508, Chofu, Tokyo, Japan
189 rdf:type schema:Organization
190 https://www.grid.ac/institutes/grid.69566.3a schema:alternateName Tohoku University
191 schema:name Graduate School of Information Sciences, Tohoku University, 6-6-01 Aramaki Aza Aoba, Aoba-ku, 980-8579, Sendai, Japan
192 New Industry Creation Hatchery Center, Tohoku University, 6-6-10, Aza-Aoba, Aramaki, Aoba-ku, 980-8579, Sendai, Miyagi, Japan
193 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...