Place Recognition for Stereo Visual Odometry using LiDAR Descriptors Article Swipe
Place recognition is a core component of Simultaneous Localization and Mapping (SLAM) algorithms. Particularly in visual SLAM systems, previously-visited places are recognized by measuring the appearance similarity between images representing these locations. However, such approaches are sensitive to visual appearance change and also can be computationally expensive. In this paper, we propose an alternative approach adapting LiDAR descriptors for 3D points obtained from stereo-visual odometry for place recognition. 3D points are potentially more reliable than 2D visual cues (e.g., 2D features) against environmental changes (e.g., variable illumination) and this may benefit visual SLAM systems in long-term deployment scenarios. Stereo-visual odometry generates 3D points with an absolute scale, which enables us to use LiDAR descriptors for place recognition with high computational efficiency. Through extensive evaluations on standard benchmark datasets, we demonstrate the accuracy, efficiency, and robustness of using 3D points for place recognition over 2D methods.
Related Topics
Concepts
Artificial intelligence
Visual odometry
Computer vision
Lidar
Robustness (evolution)
Computer science
Simultaneous localization and mapping
Odometry
Benchmark (surveying)
Stereopsis
Pattern recognition (psychology)
Robot
Mobile robot
Geography
Remote sensing
Cartography
Chemistry
Gene
Biochemistry
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://arxiv.org/pdf/1909.07267
- OA Status
- green
- References
- 27
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2973122455
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2973122455Canonical identifier for this work in OpenAlex
- Title
-
Place Recognition for Stereo Visual Odometry using LiDAR DescriptorsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-09-16Full publication date if available
- Authors
-
Jiawei Mo, Junaed SattarList of authors in order
- Landing page
-
https://arxiv.org/pdf/1909.07267Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1909.07267Direct OA link when available
- Concepts
-
Artificial intelligence, Visual odometry, Computer vision, Lidar, Robustness (evolution), Computer science, Simultaneous localization and mapping, Odometry, Benchmark (surveying), Stereopsis, Pattern recognition (psychology), Robot, Mobile robot, Geography, Remote sensing, Cartography, Chemistry, Gene, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
27Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2973122455 |
|---|---|
| doi | |
| ids.mag | 2973122455 |
| ids.openalex | https://openalex.org/W2973122455 |
| fwci | |
| type | preprint |
| title | Place Recognition for Stereo Visual Odometry using LiDAR Descriptors |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10191 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 1.0 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2202 |
| topics[0].subfield.display_name | Aerospace Engineering |
| topics[0].display_name | Robotics and Sensor-Based Localization |
| topics[1].id | https://openalex.org/T10326 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9980999827384949 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2208 |
| topics[1].subfield.display_name | Electrical and Electronic Engineering |
| topics[1].display_name | Indoor and Outdoor Localization Technologies |
| topics[2].id | https://openalex.org/T10627 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9962999820709229 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1707 |
| topics[2].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[2].display_name | Advanced Image and Video Retrieval Techniques |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C154945302 |
| concepts[0].level | 1 |
| concepts[0].score | 0.8139016032218933 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[0].display_name | Artificial intelligence |
| concepts[1].id | https://openalex.org/C5799516 |
| concepts[1].level | 3 |
| concepts[1].score | 0.7783846855163574 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q4110915 |
| concepts[1].display_name | Visual odometry |
| concepts[2].id | https://openalex.org/C31972630 |
| concepts[2].level | 1 |
| concepts[2].score | 0.7288818955421448 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[2].display_name | Computer vision |
| concepts[3].id | https://openalex.org/C51399673 |
| concepts[3].level | 2 |
| concepts[3].score | 0.7278740406036377 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q504027 |
| concepts[3].display_name | Lidar |
| concepts[4].id | https://openalex.org/C63479239 |
| concepts[4].level | 3 |
| concepts[4].score | 0.7272878289222717 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q7353546 |
| concepts[4].display_name | Robustness (evolution) |
| concepts[5].id | https://openalex.org/C41008148 |
| concepts[5].level | 0 |
| concepts[5].score | 0.7032313346862793 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[5].display_name | Computer science |
| concepts[6].id | https://openalex.org/C86369673 |
| concepts[6].level | 4 |
| concepts[6].score | 0.6726658344268799 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1203659 |
| concepts[6].display_name | Simultaneous localization and mapping |
| concepts[7].id | https://openalex.org/C49441653 |
| concepts[7].level | 4 |
| concepts[7].score | 0.6625438332557678 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q2014717 |
| concepts[7].display_name | Odometry |
| concepts[8].id | https://openalex.org/C185798385 |
| concepts[8].level | 2 |
| concepts[8].score | 0.5088746547698975 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q1161707 |
| concepts[8].display_name | Benchmark (surveying) |
| concepts[9].id | https://openalex.org/C68537008 |
| concepts[9].level | 2 |
| concepts[9].score | 0.43030866980552673 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q247932 |
| concepts[9].display_name | Stereopsis |
| concepts[10].id | https://openalex.org/C153180895 |
| concepts[10].level | 2 |
| concepts[10].score | 0.3684840500354767 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[10].display_name | Pattern recognition (psychology) |
| concepts[11].id | https://openalex.org/C90509273 |
| concepts[11].level | 2 |
| concepts[11].score | 0.2476484477519989 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q11012 |
| concepts[11].display_name | Robot |
| concepts[12].id | https://openalex.org/C19966478 |
| concepts[12].level | 3 |
| concepts[12].score | 0.17361414432525635 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q4810574 |
| concepts[12].display_name | Mobile robot |
| concepts[13].id | https://openalex.org/C205649164 |
| concepts[13].level | 0 |
| concepts[13].score | 0.16873547434806824 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[13].display_name | Geography |
| concepts[14].id | https://openalex.org/C62649853 |
| concepts[14].level | 1 |
| concepts[14].score | 0.1641598343849182 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q199687 |
| concepts[14].display_name | Remote sensing |
| concepts[15].id | https://openalex.org/C58640448 |
| concepts[15].level | 1 |
| concepts[15].score | 0.08002093434333801 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q42515 |
| concepts[15].display_name | Cartography |
| concepts[16].id | https://openalex.org/C185592680 |
| concepts[16].level | 0 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q2329 |
| concepts[16].display_name | Chemistry |
| concepts[17].id | https://openalex.org/C104317684 |
| concepts[17].level | 2 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q7187 |
| concepts[17].display_name | Gene |
| concepts[18].id | https://openalex.org/C55493867 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q7094 |
| concepts[18].display_name | Biochemistry |
| keywords[0].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[0].score | 0.8139016032218933 |
| keywords[0].display_name | Artificial intelligence |
| keywords[1].id | https://openalex.org/keywords/visual-odometry |
| keywords[1].score | 0.7783846855163574 |
| keywords[1].display_name | Visual odometry |
| keywords[2].id | https://openalex.org/keywords/computer-vision |
| keywords[2].score | 0.7288818955421448 |
| keywords[2].display_name | Computer vision |
| keywords[3].id | https://openalex.org/keywords/lidar |
| keywords[3].score | 0.7278740406036377 |
| keywords[3].display_name | Lidar |
| keywords[4].id | https://openalex.org/keywords/robustness |
| keywords[4].score | 0.7272878289222717 |
| keywords[4].display_name | Robustness (evolution) |
| keywords[5].id | https://openalex.org/keywords/computer-science |
| keywords[5].score | 0.7032313346862793 |
| keywords[5].display_name | Computer science |
| keywords[6].id | https://openalex.org/keywords/simultaneous-localization-and-mapping |
| keywords[6].score | 0.6726658344268799 |
| keywords[6].display_name | Simultaneous localization and mapping |
| keywords[7].id | https://openalex.org/keywords/odometry |
| keywords[7].score | 0.6625438332557678 |
| keywords[7].display_name | Odometry |
| keywords[8].id | https://openalex.org/keywords/benchmark |
| keywords[8].score | 0.5088746547698975 |
| keywords[8].display_name | Benchmark (surveying) |
| keywords[9].id | https://openalex.org/keywords/stereopsis |
| keywords[9].score | 0.43030866980552673 |
| keywords[9].display_name | Stereopsis |
| keywords[10].id | https://openalex.org/keywords/pattern-recognition |
| keywords[10].score | 0.3684840500354767 |
| keywords[10].display_name | Pattern recognition (psychology) |
| keywords[11].id | https://openalex.org/keywords/robot |
| keywords[11].score | 0.2476484477519989 |
| keywords[11].display_name | Robot |
| keywords[12].id | https://openalex.org/keywords/mobile-robot |
| keywords[12].score | 0.17361414432525635 |
| keywords[12].display_name | Mobile robot |
| keywords[13].id | https://openalex.org/keywords/geography |
| keywords[13].score | 0.16873547434806824 |
| keywords[13].display_name | Geography |
| keywords[14].id | https://openalex.org/keywords/remote-sensing |
| keywords[14].score | 0.1641598343849182 |
| keywords[14].display_name | Remote sensing |
| keywords[15].id | https://openalex.org/keywords/cartography |
| keywords[15].score | 0.08002093434333801 |
| keywords[15].display_name | Cartography |
| language | en |
| locations[0].id | mag:2973122455 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | submittedVersion |
| locations[0].raw_type | |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | arXiv (Cornell University) |
| locations[0].landing_page_url | https://arxiv.org/pdf/1909.07267 |
| authorships[0].author.id | https://openalex.org/A5087799311 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-1043-5353 |
| authorships[0].author.display_name | Jiawei Mo |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I2800403580 |
| authorships[0].affiliations[0].raw_affiliation_string | university of minnesota; |
| authorships[0].institutions[0].id | https://openalex.org/I2800403580 |
| authorships[0].institutions[0].ror | https://ror.org/03grvy078 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I2800403580 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | University of Minnesota System |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Jiawei Mo |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | university of minnesota; |
| authorships[1].author.id | https://openalex.org/A5055649336 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-3983-6265 |
| authorships[1].author.display_name | Junaed Sattar |
| authorships[1].affiliations[0].raw_affiliation_string | Computer Science and Engineering |
| authorships[1].author_position | last |
| authorships[1].raw_author_name | Junaed Sattar |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Computer Science and Engineering |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/1909.07267 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Place Recognition for Stereo Visual Odometry using LiDAR Descriptors |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-10-10T17:16:08.811792 |
| primary_topic.id | https://openalex.org/T10191 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 1.0 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2202 |
| primary_topic.subfield.display_name | Aerospace Engineering |
| primary_topic.display_name | Robotics and Sensor-Based Localization |
| related_works | https://openalex.org/W3131292969, https://openalex.org/W3139115362, https://openalex.org/W3238102, https://openalex.org/W2995819654, https://openalex.org/W2739574393, https://openalex.org/W1985212407, https://openalex.org/W3039208064, https://openalex.org/W2762237598, https://openalex.org/W2786604654, https://openalex.org/W3090091644, https://openalex.org/W2950381856, https://openalex.org/W3089353397, https://openalex.org/W2906829005, https://openalex.org/W2616208003, https://openalex.org/W2972748115, https://openalex.org/W3118927214, https://openalex.org/W2902610326, https://openalex.org/W2954174912, https://openalex.org/W3005683471, https://openalex.org/W3127697207 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | mag:2973122455 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | arXiv (Cornell University) |
| best_oa_location.landing_page_url | https://arxiv.org/pdf/1909.07267 |
| primary_location.id | mag:2973122455 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | submittedVersion |
| primary_location.raw_type | |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | arXiv (Cornell University) |
| primary_location.landing_page_url | https://arxiv.org/pdf/1909.07267 |
| publication_date | 2019-09-16 |
| publication_year | 2019 |
| referenced_works | https://openalex.org/W2952979613, https://openalex.org/W1989484209, https://openalex.org/W2770683484, https://openalex.org/W2795728081, https://openalex.org/W2085261163, https://openalex.org/W2012592962, https://openalex.org/W2131846894, https://openalex.org/W2115579991, https://openalex.org/W1650975994, https://openalex.org/W3102327032, https://openalex.org/W2144824356, https://openalex.org/W2565998037, https://openalex.org/W2147238549, https://openalex.org/W2160643963, https://openalex.org/W2083624955, https://openalex.org/W1972671602, https://openalex.org/W1619178479, https://openalex.org/W2953162549, https://openalex.org/W3004363006, https://openalex.org/W2620629206, https://openalex.org/W1532257412, https://openalex.org/W2139114878, https://openalex.org/W2110405746, https://openalex.org/W2099606917, https://openalex.org/W2415131061, https://openalex.org/W2910489334, https://openalex.org/W2558027072 |
| referenced_works_count | 27 |
| abstract_inverted_index.a | 3 |
| abstract_inverted_index.2D | 75, 79, 143 |
| abstract_inverted_index.3D | 59, 68, 101, 137 |
| abstract_inverted_index.In | 47 |
| abstract_inverted_index.an | 52, 104 |
| abstract_inverted_index.be | 44 |
| abstract_inverted_index.by | 22 |
| abstract_inverted_index.in | 14, 94 |
| abstract_inverted_index.is | 2 |
| abstract_inverted_index.of | 6, 135 |
| abstract_inverted_index.on | 124 |
| abstract_inverted_index.to | 37, 110 |
| abstract_inverted_index.us | 109 |
| abstract_inverted_index.we | 50, 128 |
| abstract_inverted_index.and | 9, 41, 87, 133 |
| abstract_inverted_index.are | 20, 35, 70 |
| abstract_inverted_index.can | 43 |
| abstract_inverted_index.for | 58, 65, 114, 139 |
| abstract_inverted_index.may | 89 |
| abstract_inverted_index.the | 24, 130 |
| abstract_inverted_index.use | 111 |
| abstract_inverted_index.SLAM | 16, 92 |
| abstract_inverted_index.also | 42 |
| abstract_inverted_index.core | 4 |
| abstract_inverted_index.cues | 77 |
| abstract_inverted_index.from | 62 |
| abstract_inverted_index.high | 118 |
| abstract_inverted_index.more | 72 |
| abstract_inverted_index.over | 142 |
| abstract_inverted_index.such | 33 |
| abstract_inverted_index.than | 74 |
| abstract_inverted_index.this | 48, 88 |
| abstract_inverted_index.with | 103, 117 |
| abstract_inverted_index.LiDAR | 56, 112 |
| abstract_inverted_index.Place | 0 |
| abstract_inverted_index.place | 66, 115, 140 |
| abstract_inverted_index.these | 30 |
| abstract_inverted_index.using | 136 |
| abstract_inverted_index.which | 107 |
| abstract_inverted_index.(SLAM) | 11 |
| abstract_inverted_index.(e.g., | 78, 84 |
| abstract_inverted_index.change | 40 |
| abstract_inverted_index.images | 28 |
| abstract_inverted_index.paper, | 49 |
| abstract_inverted_index.places | 19 |
| abstract_inverted_index.points | 60, 69, 102, 138 |
| abstract_inverted_index.scale, | 106 |
| abstract_inverted_index.visual | 15, 38, 76, 91 |
| abstract_inverted_index.Mapping | 10 |
| abstract_inverted_index.Through | 121 |
| abstract_inverted_index.against | 81 |
| abstract_inverted_index.benefit | 90 |
| abstract_inverted_index.between | 27 |
| abstract_inverted_index.changes | 83 |
| abstract_inverted_index.enables | 108 |
| abstract_inverted_index.propose | 51 |
| abstract_inverted_index.systems | 93 |
| abstract_inverted_index.However, | 32 |
| abstract_inverted_index.absolute | 105 |
| abstract_inverted_index.adapting | 55 |
| abstract_inverted_index.approach | 54 |
| abstract_inverted_index.methods. | 144 |
| abstract_inverted_index.obtained | 61 |
| abstract_inverted_index.odometry | 64, 99 |
| abstract_inverted_index.reliable | 73 |
| abstract_inverted_index.standard | 125 |
| abstract_inverted_index.systems, | 17 |
| abstract_inverted_index.variable | 85 |
| abstract_inverted_index.accuracy, | 131 |
| abstract_inverted_index.benchmark | 126 |
| abstract_inverted_index.component | 5 |
| abstract_inverted_index.datasets, | 127 |
| abstract_inverted_index.extensive | 122 |
| abstract_inverted_index.features) | 80 |
| abstract_inverted_index.generates | 100 |
| abstract_inverted_index.long-term | 95 |
| abstract_inverted_index.measuring | 23 |
| abstract_inverted_index.sensitive | 36 |
| abstract_inverted_index.appearance | 25, 39 |
| abstract_inverted_index.approaches | 34 |
| abstract_inverted_index.deployment | 96 |
| abstract_inverted_index.expensive. | 46 |
| abstract_inverted_index.locations. | 31 |
| abstract_inverted_index.recognized | 21 |
| abstract_inverted_index.robustness | 134 |
| abstract_inverted_index.scenarios. | 97 |
| abstract_inverted_index.similarity | 26 |
| abstract_inverted_index.algorithms. | 12 |
| abstract_inverted_index.alternative | 53 |
| abstract_inverted_index.demonstrate | 129 |
| abstract_inverted_index.descriptors | 57, 113 |
| abstract_inverted_index.efficiency, | 132 |
| abstract_inverted_index.efficiency. | 120 |
| abstract_inverted_index.evaluations | 123 |
| abstract_inverted_index.potentially | 71 |
| abstract_inverted_index.recognition | 1, 116, 141 |
| abstract_inverted_index.Localization | 8 |
| abstract_inverted_index.Particularly | 13 |
| abstract_inverted_index.Simultaneous | 7 |
| abstract_inverted_index.recognition. | 67 |
| abstract_inverted_index.representing | 29 |
| abstract_inverted_index.Stereo-visual | 98 |
| abstract_inverted_index.computational | 119 |
| abstract_inverted_index.environmental | 82 |
| abstract_inverted_index.illumination) | 86 |
| abstract_inverted_index.stereo-visual | 63 |
| abstract_inverted_index.computationally | 45 |
| abstract_inverted_index.previously-visited | 18 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile |