Improving indoor visual navigation generalization with scene priors and Markov relational reasoning
Abstract The problem of visual navigation is the poor generalization to find the given target object in unexplored environment without the help of auxiliary sensors. We propose solving the visual navigation problem by incorporating object spatial scene priors and visible object relational reasoning....
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Gespeichert in:
| Autor*in: |
Zhou, Kang [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Applied intelligence - Springer US, 1991, 52(2022), 15 vom: 04. Apr., Seite 17600-17613 |
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| Übergeordnetes Werk: |
volume:52 ; year:2022 ; number:15 ; day:04 ; month:04 ; pages:17600-17613 |
| Links: |
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| DOI / URN: |
10.1007/s10489-022-03317-6 |
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OLC208001711X |
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| 520 | |a Abstract The problem of visual navigation is the poor generalization to find the given target object in unexplored environment without the help of auxiliary sensors. We propose solving the visual navigation problem by incorporating object spatial scene priors and visible object relational reasoning. To get more accurate ground truth environment priors, we construct specific scene graph priors for indoor navigation, which provides rich object spatial relationships for helping finding the target objects by object relation detection. Furthermore, to imitate human’s reasonability in searching objects, we encode our scene graph priors with Markov model for relational reasoning and fuse them into reinforcement learning policy network, which improves model generalization in novel scenes. Moreover, we perform experiments on the AI2THOR virtual environment and outperform the current most state-of-the-art both in SPL (Success weighted by Path Length) and success rate on average. | ||
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10.1007/s10489-022-03317-6 doi (DE-627)OLC208001711X (DE-He213)s10489-022-03317-6-p DE-627 ger DE-627 rakwb eng 004 VZ Zhou, Kang verfasserin (orcid)0000-0002-4177-7188 aut Improving indoor visual navigation generalization with scene priors and Markov relational reasoning 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract The problem of visual navigation is the poor generalization to find the given target object in unexplored environment without the help of auxiliary sensors. We propose solving the visual navigation problem by incorporating object spatial scene priors and visible object relational reasoning. To get more accurate ground truth environment priors, we construct specific scene graph priors for indoor navigation, which provides rich object spatial relationships for helping finding the target objects by object relation detection. Furthermore, to imitate human’s reasonability in searching objects, we encode our scene graph priors with Markov model for relational reasoning and fuse them into reinforcement learning policy network, which improves model generalization in novel scenes. Moreover, we perform experiments on the AI2THOR virtual environment and outperform the current most state-of-the-art both in SPL (Success weighted by Path Length) and success rate on average. Visual navigation Scene graph Markov relational reasoning Reinforcement learning Guo, Chi aut Zhang, Huyin aut Enthalten in Applied intelligence Springer US, 1991 52(2022), 15 vom: 04. Apr., Seite 17600-17613 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:52 year:2022 number:15 day:04 month:04 pages:17600-17613 https://doi.org/10.1007/s10489-022-03317-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 52 2022 15 04 04 17600-17613 |
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10.1007/s10489-022-03317-6 doi (DE-627)OLC208001711X (DE-He213)s10489-022-03317-6-p DE-627 ger DE-627 rakwb eng 004 VZ Zhou, Kang verfasserin (orcid)0000-0002-4177-7188 aut Improving indoor visual navigation generalization with scene priors and Markov relational reasoning 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract The problem of visual navigation is the poor generalization to find the given target object in unexplored environment without the help of auxiliary sensors. We propose solving the visual navigation problem by incorporating object spatial scene priors and visible object relational reasoning. To get more accurate ground truth environment priors, we construct specific scene graph priors for indoor navigation, which provides rich object spatial relationships for helping finding the target objects by object relation detection. Furthermore, to imitate human’s reasonability in searching objects, we encode our scene graph priors with Markov model for relational reasoning and fuse them into reinforcement learning policy network, which improves model generalization in novel scenes. Moreover, we perform experiments on the AI2THOR virtual environment and outperform the current most state-of-the-art both in SPL (Success weighted by Path Length) and success rate on average. Visual navigation Scene graph Markov relational reasoning Reinforcement learning Guo, Chi aut Zhang, Huyin aut Enthalten in Applied intelligence Springer US, 1991 52(2022), 15 vom: 04. Apr., Seite 17600-17613 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:52 year:2022 number:15 day:04 month:04 pages:17600-17613 https://doi.org/10.1007/s10489-022-03317-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 52 2022 15 04 04 17600-17613 |
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10.1007/s10489-022-03317-6 doi (DE-627)OLC208001711X (DE-He213)s10489-022-03317-6-p DE-627 ger DE-627 rakwb eng 004 VZ Zhou, Kang verfasserin (orcid)0000-0002-4177-7188 aut Improving indoor visual navigation generalization with scene priors and Markov relational reasoning 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract The problem of visual navigation is the poor generalization to find the given target object in unexplored environment without the help of auxiliary sensors. We propose solving the visual navigation problem by incorporating object spatial scene priors and visible object relational reasoning. To get more accurate ground truth environment priors, we construct specific scene graph priors for indoor navigation, which provides rich object spatial relationships for helping finding the target objects by object relation detection. Furthermore, to imitate human’s reasonability in searching objects, we encode our scene graph priors with Markov model for relational reasoning and fuse them into reinforcement learning policy network, which improves model generalization in novel scenes. Moreover, we perform experiments on the AI2THOR virtual environment and outperform the current most state-of-the-art both in SPL (Success weighted by Path Length) and success rate on average. Visual navigation Scene graph Markov relational reasoning Reinforcement learning Guo, Chi aut Zhang, Huyin aut Enthalten in Applied intelligence Springer US, 1991 52(2022), 15 vom: 04. Apr., Seite 17600-17613 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:52 year:2022 number:15 day:04 month:04 pages:17600-17613 https://doi.org/10.1007/s10489-022-03317-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 52 2022 15 04 04 17600-17613 |
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10.1007/s10489-022-03317-6 doi (DE-627)OLC208001711X (DE-He213)s10489-022-03317-6-p DE-627 ger DE-627 rakwb eng 004 VZ Zhou, Kang verfasserin (orcid)0000-0002-4177-7188 aut Improving indoor visual navigation generalization with scene priors and Markov relational reasoning 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract The problem of visual navigation is the poor generalization to find the given target object in unexplored environment without the help of auxiliary sensors. We propose solving the visual navigation problem by incorporating object spatial scene priors and visible object relational reasoning. To get more accurate ground truth environment priors, we construct specific scene graph priors for indoor navigation, which provides rich object spatial relationships for helping finding the target objects by object relation detection. Furthermore, to imitate human’s reasonability in searching objects, we encode our scene graph priors with Markov model for relational reasoning and fuse them into reinforcement learning policy network, which improves model generalization in novel scenes. Moreover, we perform experiments on the AI2THOR virtual environment and outperform the current most state-of-the-art both in SPL (Success weighted by Path Length) and success rate on average. Visual navigation Scene graph Markov relational reasoning Reinforcement learning Guo, Chi aut Zhang, Huyin aut Enthalten in Applied intelligence Springer US, 1991 52(2022), 15 vom: 04. Apr., Seite 17600-17613 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:52 year:2022 number:15 day:04 month:04 pages:17600-17613 https://doi.org/10.1007/s10489-022-03317-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 52 2022 15 04 04 17600-17613 |
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Abstract The problem of visual navigation is the poor generalization to find the given target object in unexplored environment without the help of auxiliary sensors. We propose solving the visual navigation problem by incorporating object spatial scene priors and visible object relational reasoning. To get more accurate ground truth environment priors, we construct specific scene graph priors for indoor navigation, which provides rich object spatial relationships for helping finding the target objects by object relation detection. Furthermore, to imitate human’s reasonability in searching objects, we encode our scene graph priors with Markov model for relational reasoning and fuse them into reinforcement learning policy network, which improves model generalization in novel scenes. Moreover, we perform experiments on the AI2THOR virtual environment and outperform the current most state-of-the-art both in SPL (Success weighted by Path Length) and success rate on average. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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Abstract The problem of visual navigation is the poor generalization to find the given target object in unexplored environment without the help of auxiliary sensors. We propose solving the visual navigation problem by incorporating object spatial scene priors and visible object relational reasoning. To get more accurate ground truth environment priors, we construct specific scene graph priors for indoor navigation, which provides rich object spatial relationships for helping finding the target objects by object relation detection. Furthermore, to imitate human’s reasonability in searching objects, we encode our scene graph priors with Markov model for relational reasoning and fuse them into reinforcement learning policy network, which improves model generalization in novel scenes. Moreover, we perform experiments on the AI2THOR virtual environment and outperform the current most state-of-the-art both in SPL (Success weighted by Path Length) and success rate on average. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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Abstract The problem of visual navigation is the poor generalization to find the given target object in unexplored environment without the help of auxiliary sensors. We propose solving the visual navigation problem by incorporating object spatial scene priors and visible object relational reasoning. To get more accurate ground truth environment priors, we construct specific scene graph priors for indoor navigation, which provides rich object spatial relationships for helping finding the target objects by object relation detection. Furthermore, to imitate human’s reasonability in searching objects, we encode our scene graph priors with Markov model for relational reasoning and fuse them into reinforcement learning policy network, which improves model generalization in novel scenes. Moreover, we perform experiments on the AI2THOR virtual environment and outperform the current most state-of-the-art both in SPL (Success weighted by Path Length) and success rate on average. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC208001711X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230506090828.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230131s2022 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10489-022-03317-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC208001711X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10489-022-03317-6-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhou, Kang</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-4177-7188</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Improving indoor visual navigation generalization with scene priors and Markov relational reasoning</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The problem of visual navigation is the poor generalization to find the given target object in unexplored environment without the help of auxiliary sensors. We propose solving the visual navigation problem by incorporating object spatial scene priors and visible object relational reasoning. To get more accurate ground truth environment priors, we construct specific scene graph priors for indoor navigation, which provides rich object spatial relationships for helping finding the target objects by object relation detection. Furthermore, to imitate human’s reasonability in searching objects, we encode our scene graph priors with Markov model for relational reasoning and fuse them into reinforcement learning policy network, which improves model generalization in novel scenes. Moreover, we perform experiments on the AI2THOR virtual environment and outperform the current most state-of-the-art both in SPL (Success weighted by Path Length) and success rate on average.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Visual navigation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Scene graph</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Markov relational reasoning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Reinforcement learning</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Guo, Chi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Huyin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Applied intelligence</subfield><subfield code="d">Springer US, 1991</subfield><subfield code="g">52(2022), 15 vom: 04. Apr., Seite 17600-17613</subfield><subfield code="w">(DE-627)130990515</subfield><subfield code="w">(DE-600)1080229-0</subfield><subfield code="w">(DE-576)029154286</subfield><subfield code="x">0924-669X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:52</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:15</subfield><subfield code="g">day:04</subfield><subfield code="g">month:04</subfield><subfield code="g">pages:17600-17613</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10489-022-03317-6</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">52</subfield><subfield code="j">2022</subfield><subfield code="e">15</subfield><subfield code="b">04</subfield><subfield code="c">04</subfield><subfield code="h">17600-17613</subfield></datafield></record></collection>
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