Visual Bearing-Only Simultaneous Localization and Mapping with Improved Feature Matching View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2007

AUTHORS

Hauke Strasdat , Cyrill Stachniss , Maren Bennewitz , Wolfram Burgard

ABSTRACT

In this this paper, we present a solution to the simultaneous localization and mapping (SLAM) problem for a robot equipped with a single perspective camera. We track extracted features over multiple frames to estimate the depth information. To represent the joint posterior about the trajectory of the robot and a map of the environment, we apply a Rao-Blackwellized particle filter. We present a novel method to match features using a cost function that takes into account differences between the feature descriptor vectors as well as spatial information. To find an optimal matching between observed features, we apply a global optimization algorithm. Experimental results obtained with a real robot show that our approach is robust and tolerant to noise in the odometry information of the robot. Furthermore, we present experiments that demonstrate the superior performance of our feature matching technique compared to other approaches. More... »

PAGES

15-21

Book

TITLE

Autonome Mobile Systeme 2007

ISBN

978-3-540-74763-5
978-3-540-74764-2

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-74764-2_3

DOI

http://dx.doi.org/10.1007/978-3-540-74764-2_3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1033785014


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": "Computer Science Institute, University of Freiburg, Germany", 
          "id": "http://www.grid.ac/institutes/grid.5963.9", 
          "name": [
            "Computer Science Institute, University of Freiburg, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Strasdat", 
        "givenName": "Hauke", 
        "id": "sg:person.010640440533.15", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010640440533.15"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Computer Science Institute, University of Freiburg, Germany", 
          "id": "http://www.grid.ac/institutes/grid.5963.9", 
          "name": [
            "Computer Science Institute, University of 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"
      }, 
      {
        "affiliation": {
          "alternateName": "Computer Science Institute, University of Freiburg, Germany", 
          "id": "http://www.grid.ac/institutes/grid.5963.9", 
          "name": [
            "Computer Science Institute, University of Freiburg, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bennewitz", 
        "givenName": "Maren", 
        "id": "sg:person.015617505777.45", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015617505777.45"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Computer Science Institute, University of Freiburg, Germany", 
          "id": "http://www.grid.ac/institutes/grid.5963.9", 
          "name": [
            "Computer Science Institute, University of Freiburg, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Burgard", 
        "givenName": "Wolfram", 
        "id": "sg:person.014270043511.25", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014270043511.25"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2007", 
    "datePublishedReg": "2007-01-01", 
    "description": "In this this paper, we present a solution to the simultaneous localization and mapping (SLAM) problem for a robot equipped with a single perspective camera. We track extracted features over multiple frames to estimate the depth information. To represent the joint posterior about the trajectory of the robot and a map of the environment, we apply a Rao-Blackwellized particle filter. We present a novel method to match features using a cost function that takes into account differences between the feature descriptor vectors as well as spatial information. To find an optimal matching between observed features, we apply a global optimization algorithm. Experimental results obtained with a real robot show that our approach is robust and tolerant to noise in the odometry information of the robot. Furthermore, we present experiments that demonstrate the superior performance of our feature matching technique compared to other approaches.", 
    "editor": [
      {
        "familyName": "Berns", 
        "givenName": "Karsten", 
        "type": "Person"
      }, 
      {
        "familyName": "Luksch", 
        "givenName": "Tobias", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-540-74764-2_3", 
    "isAccessibleForFree": true, 
    "isPartOf": {
      "isbn": [
        "978-3-540-74763-5", 
        "978-3-540-74764-2"
      ], 
      "name": "Autonome Mobile Systeme 2007", 
      "type": "Book"
    }, 
    "keywords": [
      "simultaneous localization", 
      "single perspective camera", 
      "particle filter", 
      "odometry information", 
      "experimental results", 
      "robot show", 
      "feature matching technique", 
      "superior performance", 
      "robot", 
      "optimization algorithm", 
      "cost function", 
      "novel method", 
      "feature matching", 
      "spatial information", 
      "multiple frames", 
      "matching technique", 
      "mapping problem", 
      "global optimization algorithm", 
      "feature descriptor vector", 
      "observed features", 
      "filter", 
      "bearing", 
      "visual bearings", 
      "perspective camera", 
      "depth information", 
      "real robot show", 
      "optimal matching", 
      "performance", 
      "camera", 
      "matching", 
      "solution", 
      "descriptor vectors", 
      "technique", 
      "approach", 
      "experiments", 
      "features", 
      "method", 
      "Rao", 
      "algorithm", 
      "frame", 
      "trajectories", 
      "results", 
      "problem", 
      "show", 
      "environment", 
      "maps", 
      "information", 
      "mapping", 
      "localization", 
      "vector", 
      "joint posterior", 
      "function", 
      "account differences", 
      "differences", 
      "posterior", 
      "paper"
    ], 
    "name": "Visual Bearing-Only Simultaneous Localization and Mapping with Improved Feature Matching", 
    "pagination": "15-21", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1033785014"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-540-74764-2_3"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-540-74764-2_3", 
      "https://app.dimensions.ai/details/publication/pub.1033785014"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-11-24T21:11", 
    "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/chapter/chapter_137.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-540-74764-2_3"
  }
]
 

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.10.1007/978-3-540-74764-2_3'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-74764-2_3'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-74764-2_3'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-74764-2_3'


 

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

141 TRIPLES      22 PREDICATES      81 URIs      74 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-540-74764-2_3 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N03f8d6807aa04fbbabdb9e62937e336d
4 schema:datePublished 2007
5 schema:datePublishedReg 2007-01-01
6 schema:description In this this paper, we present a solution to the simultaneous localization and mapping (SLAM) problem for a robot equipped with a single perspective camera. We track extracted features over multiple frames to estimate the depth information. To represent the joint posterior about the trajectory of the robot and a map of the environment, we apply a Rao-Blackwellized particle filter. We present a novel method to match features using a cost function that takes into account differences between the feature descriptor vectors as well as spatial information. To find an optimal matching between observed features, we apply a global optimization algorithm. Experimental results obtained with a real robot show that our approach is robust and tolerant to noise in the odometry information of the robot. Furthermore, we present experiments that demonstrate the superior performance of our feature matching technique compared to other approaches.
7 schema:editor N89896a77c8c442179226c11dbb7acec1
8 schema:genre chapter
9 schema:isAccessibleForFree true
10 schema:isPartOf Nd5b48a1457c44ee5945ecdecaf527cb3
11 schema:keywords Rao
12 account differences
13 algorithm
14 approach
15 bearing
16 camera
17 cost function
18 depth information
19 descriptor vectors
20 differences
21 environment
22 experimental results
23 experiments
24 feature descriptor vector
25 feature matching
26 feature matching technique
27 features
28 filter
29 frame
30 function
31 global optimization algorithm
32 information
33 joint posterior
34 localization
35 mapping
36 mapping problem
37 maps
38 matching
39 matching technique
40 method
41 multiple frames
42 novel method
43 observed features
44 odometry information
45 optimal matching
46 optimization algorithm
47 paper
48 particle filter
49 performance
50 perspective camera
51 posterior
52 problem
53 real robot show
54 results
55 robot
56 robot show
57 show
58 simultaneous localization
59 single perspective camera
60 solution
61 spatial information
62 superior performance
63 technique
64 trajectories
65 vector
66 visual bearings
67 schema:name Visual Bearing-Only Simultaneous Localization and Mapping with Improved Feature Matching
68 schema:pagination 15-21
69 schema:productId N3a31530ca4b642ef8c012d42a0b728d5
70 N915b9150d0a24e87b553e0846406df83
71 schema:publisher N7ff9ce63811b4584bef6e1da7c67ad51
72 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033785014
73 https://doi.org/10.1007/978-3-540-74764-2_3
74 schema:sdDatePublished 2022-11-24T21:11
75 schema:sdLicense https://scigraph.springernature.com/explorer/license/
76 schema:sdPublisher N03c8289ca4964c699b09f64861d76c4b
77 schema:url https://doi.org/10.1007/978-3-540-74764-2_3
78 sgo:license sg:explorer/license/
79 sgo:sdDataset chapters
80 rdf:type schema:Chapter
81 N03c8289ca4964c699b09f64861d76c4b schema:name Springer Nature - SN SciGraph project
82 rdf:type schema:Organization
83 N03f8d6807aa04fbbabdb9e62937e336d rdf:first sg:person.010640440533.15
84 rdf:rest Ne40aa8f833ba4e1f82d1b7eca200dd6f
85 N1e565d6456b74e119cc4d3509c658353 rdf:first sg:person.014270043511.25
86 rdf:rest rdf:nil
87 N3a31530ca4b642ef8c012d42a0b728d5 schema:name dimensions_id
88 schema:value pub.1033785014
89 rdf:type schema:PropertyValue
90 N43be81613a3f43e99fd613ec2e683969 rdf:first sg:person.015617505777.45
91 rdf:rest N1e565d6456b74e119cc4d3509c658353
92 N44529f1e712243b6a101aeeccdafb81f schema:familyName Berns
93 schema:givenName Karsten
94 rdf:type schema:Person
95 N7ff9ce63811b4584bef6e1da7c67ad51 schema:name Springer Nature
96 rdf:type schema:Organisation
97 N89896a77c8c442179226c11dbb7acec1 rdf:first N44529f1e712243b6a101aeeccdafb81f
98 rdf:rest Na43ca8da44fd41769d58c09dff3f1131
99 N915b9150d0a24e87b553e0846406df83 schema:name doi
100 schema:value 10.1007/978-3-540-74764-2_3
101 rdf:type schema:PropertyValue
102 Na43ca8da44fd41769d58c09dff3f1131 rdf:first Nb32c6f826fb845679a0e13641f77cd59
103 rdf:rest rdf:nil
104 Nb32c6f826fb845679a0e13641f77cd59 schema:familyName Luksch
105 schema:givenName Tobias
106 rdf:type schema:Person
107 Nd5b48a1457c44ee5945ecdecaf527cb3 schema:isbn 978-3-540-74763-5
108 978-3-540-74764-2
109 schema:name Autonome Mobile Systeme 2007
110 rdf:type schema:Book
111 Ne40aa8f833ba4e1f82d1b7eca200dd6f rdf:first sg:person.015152144445.37
112 rdf:rest N43be81613a3f43e99fd613ec2e683969
113 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
114 schema:name Information and Computing Sciences
115 rdf:type schema:DefinedTerm
116 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
117 schema:name Artificial Intelligence and Image Processing
118 rdf:type schema:DefinedTerm
119 sg:person.010640440533.15 schema:affiliation grid-institutes:grid.5963.9
120 schema:familyName Strasdat
121 schema:givenName Hauke
122 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010640440533.15
123 rdf:type schema:Person
124 sg:person.014270043511.25 schema:affiliation grid-institutes:grid.5963.9
125 schema:familyName Burgard
126 schema:givenName Wolfram
127 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014270043511.25
128 rdf:type schema:Person
129 sg:person.015152144445.37 schema:affiliation grid-institutes:grid.5963.9
130 schema:familyName Stachniss
131 schema:givenName Cyrill
132 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015152144445.37
133 rdf:type schema:Person
134 sg:person.015617505777.45 schema:affiliation grid-institutes:grid.5963.9
135 schema:familyName Bennewitz
136 schema:givenName Maren
137 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015617505777.45
138 rdf:type schema:Person
139 grid-institutes:grid.5963.9 schema:alternateName Computer Science Institute, University of Freiburg, Germany
140 schema:name Computer Science Institute, University of Freiburg, Germany
141 rdf:type schema:Organization
 




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


...