Lifelong Map Learning for Graph-based SLAM in Static Environments View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2010-05-21

AUTHORS

Henrik Kretzschmar, Giorgio Grisetti, Cyrill Stachniss

ABSTRACT

In this paper, we address the problem of lifelong map learning in static environments with mobile robots using the graph-based formulation of the simultaneous localization and mapping problem. The pose graph, which stores the poses of the robot and spatial constraints between them, is the central data structure in graph-based SLAM. The size of the pose graph has a direct influence on the runtime and the memory complexity of the SLAM system and typically grows over time. A robot that performs lifelong mapping in a bounded environment has to limit the memory and computational complexity of its mapping system. We present a novel approach to prune the pose graph so that it only grows when the robot acquires relevant new information about the environment in terms of expected information gain. As a result, our approach scales with the size of the environment and not with the length of the trajectory, which is an important prerequisite for lifelong map learning. The experiments presented in this paper illustrate the properties of our method using real robots. More... »

PAGES

199-206

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s13218-010-0034-2

DOI

http://dx.doi.org/10.1007/s13218-010-0034-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1049526038


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/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 79, 79110, Freiburg, Germany", 
          "id": "http://www.grid.ac/institutes/grid.5963.9", 
          "name": [
            "Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 79, 79110, Freiburg, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kretzschmar", 
        "givenName": "Henrik", 
        "id": "sg:person.015045121627.26", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015045121627.26"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 79, 79110, Freiburg, Germany", 
          "id": "http://www.grid.ac/institutes/grid.5963.9", 
          "name": [
            "Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 79, 79110, Freiburg, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Grisetti", 
        "givenName": "Giorgio", 
        "id": "sg:person.012500024215.02", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012500024215.02"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 79, 79110, Freiburg, Germany", 
          "id": "http://www.grid.ac/institutes/grid.5963.9", 
          "name": [
            "Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 79, 79110, Freiburg, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Stachniss", 
        "givenName": "Cyrill", 
        "id": "sg:person.015152144445.37", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015152144445.37"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1023/a:1008854305733", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025138015", 
          "https://doi.org/10.1023/a:1008854305733"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1015269615729", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017420141", 
          "https://doi.org/10.1023/a:1015269615729"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2010-05-21", 
    "datePublishedReg": "2010-05-21", 
    "description": "In this paper, we address the problem of lifelong map learning in static environments with mobile robots using the graph-based formulation of the simultaneous localization and mapping problem. The pose graph, which stores the poses of the robot and spatial constraints between them, is the central data structure in graph-based SLAM. The size of the pose graph has a direct influence on the runtime and the memory complexity of the SLAM system and typically grows over time. A\u00a0robot that performs lifelong mapping in a bounded environment has to limit the memory and computational complexity of its mapping system. We present a novel approach to prune the pose graph so that it only grows when the robot acquires relevant new information about the environment in terms of expected information gain. As a result, our approach scales with the size of the environment and not with the length of the trajectory, which is an important prerequisite for lifelong map learning. The experiments presented in this paper illustrate the properties of our method using real robots.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s13218-010-0034-2", 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.3773052", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1136548", 
        "issn": [
          "0933-1875", 
          "1610-1987"
        ], 
        "name": "KI - K\u00fcnstliche Intelligenz", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "24"
      }
    ], 
    "keywords": [
      "pose graph", 
      "static environment", 
      "map learning", 
      "central data structure", 
      "graph-based SLAM", 
      "graph-based formulation", 
      "real robot", 
      "mobile robot", 
      "lifelong mapping", 
      "SLAM system", 
      "data structure", 
      "simultaneous localization", 
      "computational complexity", 
      "memory complexity", 
      "mapping problem", 
      "information gain", 
      "robot", 
      "spatial constraints", 
      "SLAM", 
      "mapping system", 
      "graph", 
      "novel approach", 
      "learning", 
      "complexity", 
      "environment", 
      "runtime", 
      "pose", 
      "important prerequisite", 
      "relevant new information", 
      "system", 
      "information", 
      "constraints", 
      "new information", 
      "memory", 
      "maps", 
      "mapping", 
      "trajectories", 
      "experiments", 
      "method", 
      "localization", 
      "terms", 
      "time", 
      "prerequisite", 
      "results", 
      "size", 
      "gain", 
      "formulation", 
      "structure", 
      "direct influence", 
      "properties", 
      "length", 
      "influence", 
      "paper", 
      "problem", 
      "approach"
    ], 
    "name": "Lifelong Map Learning for Graph-based SLAM in Static Environments", 
    "pagination": "199-206", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1049526038"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s13218-010-0034-2"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s13218-010-0034-2", 
      "https://app.dimensions.ai/details/publication/pub.1049526038"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-11-24T20:54", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221124/entities/gbq_results/article/article_517.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s13218-010-0034-2"
  }
]
 

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.10.1007/s13218-010-0034-2'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s13218-010-0034-2'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s13218-010-0034-2'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s13218-010-0034-2'


 

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

136 TRIPLES      21 PREDICATES      81 URIs      71 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s13218-010-0034-2 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author Ncb79e411bae04397acb899c01a294b04
4 schema:citation sg:pub.10.1023/a:1008854305733
5 sg:pub.10.1023/a:1015269615729
6 schema:datePublished 2010-05-21
7 schema:datePublishedReg 2010-05-21
8 schema:description In this paper, we address the problem of lifelong map learning in static environments with mobile robots using the graph-based formulation of the simultaneous localization and mapping problem. The pose graph, which stores the poses of the robot and spatial constraints between them, is the central data structure in graph-based SLAM. The size of the pose graph has a direct influence on the runtime and the memory complexity of the SLAM system and typically grows over time. A robot that performs lifelong mapping in a bounded environment has to limit the memory and computational complexity of its mapping system. We present a novel approach to prune the pose graph so that it only grows when the robot acquires relevant new information about the environment in terms of expected information gain. As a result, our approach scales with the size of the environment and not with the length of the trajectory, which is an important prerequisite for lifelong map learning. The experiments presented in this paper illustrate the properties of our method using real robots.
9 schema:genre article
10 schema:isAccessibleForFree false
11 schema:isPartOf N0aef1a8339e44a329414dc58cdb4a959
12 N3e2ffd0395de431d96f45396e8ae60b7
13 sg:journal.1136548
14 schema:keywords SLAM
15 SLAM system
16 approach
17 central data structure
18 complexity
19 computational complexity
20 constraints
21 data structure
22 direct influence
23 environment
24 experiments
25 formulation
26 gain
27 graph
28 graph-based SLAM
29 graph-based formulation
30 important prerequisite
31 influence
32 information
33 information gain
34 learning
35 length
36 lifelong mapping
37 localization
38 map learning
39 mapping
40 mapping problem
41 mapping system
42 maps
43 memory
44 memory complexity
45 method
46 mobile robot
47 new information
48 novel approach
49 paper
50 pose
51 pose graph
52 prerequisite
53 problem
54 properties
55 real robot
56 relevant new information
57 results
58 robot
59 runtime
60 simultaneous localization
61 size
62 spatial constraints
63 static environment
64 structure
65 system
66 terms
67 time
68 trajectories
69 schema:name Lifelong Map Learning for Graph-based SLAM in Static Environments
70 schema:pagination 199-206
71 schema:productId N024082b18c034748a9c2506b280240f5
72 Nb66ec849a71745e3b84b7fd5239645e0
73 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049526038
74 https://doi.org/10.1007/s13218-010-0034-2
75 schema:sdDatePublished 2022-11-24T20:54
76 schema:sdLicense https://scigraph.springernature.com/explorer/license/
77 schema:sdPublisher Nf9621b3319c64ca2907052a72e91408e
78 schema:url https://doi.org/10.1007/s13218-010-0034-2
79 sgo:license sg:explorer/license/
80 sgo:sdDataset articles
81 rdf:type schema:ScholarlyArticle
82 N024082b18c034748a9c2506b280240f5 schema:name dimensions_id
83 schema:value pub.1049526038
84 rdf:type schema:PropertyValue
85 N0aef1a8339e44a329414dc58cdb4a959 schema:volumeNumber 24
86 rdf:type schema:PublicationVolume
87 N3e2ffd0395de431d96f45396e8ae60b7 schema:issueNumber 3
88 rdf:type schema:PublicationIssue
89 Nb66ec849a71745e3b84b7fd5239645e0 schema:name doi
90 schema:value 10.1007/s13218-010-0034-2
91 rdf:type schema:PropertyValue
92 Ncb79e411bae04397acb899c01a294b04 rdf:first sg:person.015045121627.26
93 rdf:rest Nd53b4a3d12434b1cb062741e9f9534b3
94 Ncc62f5f7d2ea499a942f7a7b0e099f90 rdf:first sg:person.015152144445.37
95 rdf:rest rdf:nil
96 Nd53b4a3d12434b1cb062741e9f9534b3 rdf:first sg:person.012500024215.02
97 rdf:rest Ncc62f5f7d2ea499a942f7a7b0e099f90
98 Nf9621b3319c64ca2907052a72e91408e schema:name Springer Nature - SN SciGraph project
99 rdf:type schema:Organization
100 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
101 schema:name Information and Computing Sciences
102 rdf:type schema:DefinedTerm
103 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
104 schema:name Artificial Intelligence and Image Processing
105 rdf:type schema:DefinedTerm
106 sg:grant.3773052 http://pending.schema.org/fundedItem sg:pub.10.1007/s13218-010-0034-2
107 rdf:type schema:MonetaryGrant
108 sg:journal.1136548 schema:issn 0933-1875
109 1610-1987
110 schema:name KI - Künstliche Intelligenz
111 schema:publisher Springer Nature
112 rdf:type schema:Periodical
113 sg:person.012500024215.02 schema:affiliation grid-institutes:grid.5963.9
114 schema:familyName Grisetti
115 schema:givenName Giorgio
116 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012500024215.02
117 rdf:type schema:Person
118 sg:person.015045121627.26 schema:affiliation grid-institutes:grid.5963.9
119 schema:familyName Kretzschmar
120 schema:givenName Henrik
121 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015045121627.26
122 rdf:type schema:Person
123 sg:person.015152144445.37 schema:affiliation grid-institutes:grid.5963.9
124 schema:familyName Stachniss
125 schema:givenName Cyrill
126 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015152144445.37
127 rdf:type schema:Person
128 sg:pub.10.1023/a:1008854305733 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025138015
129 https://doi.org/10.1023/a:1008854305733
130 rdf:type schema:CreativeWork
131 sg:pub.10.1023/a:1015269615729 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017420141
132 https://doi.org/10.1023/a:1015269615729
133 rdf:type schema:CreativeWork
134 grid-institutes:grid.5963.9 schema:alternateName Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 79, 79110, Freiburg, Germany
135 schema:name Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 79, 79110, Freiburg, Germany
136 rdf:type schema:Organization
 




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


...