DIO-SLAM: A Dynamic RGB-D SLAM Method Combining Instance Segmentation and Optical Flow Article Swipe
Lang He
,
Shiyun Li
,
Jun-Ting Qiu
,
C Zhang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/s24185929
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/s24185929
Feature points from moving objects can negatively impact the accuracy of Visual Simultaneous Localization and Mapping (VSLAM) algorithms, while detection or semantic segmentation-based VSLAM approaches often fail to accurately determine the true motion state of objects. To address this challenge, this paper introduces DIO-SLAM: Dynamic Instance Optical Flow SLAM, a VSLAM system specifically designed for dynamic environments. Initially, the detection thread employs YOLACT (You Only Look At CoefficienTs) to distinguish between rigid and non-rigid objects within the scene. Subsequently, the optical flow thread estimates optical flow and introduces a novel approach to capture the optical flow of moving objects by leveraging optical flow residuals. Following this, an optical flow consistency method is implemented to assess the dynamic nature of rigid object mask regions, classifying them as either moving or stationary rigid objects. To mitigate errors caused by missed detections or motion blur, a motion frame propagation method is employed. Lastly, a dense mapping thread is incorporated to filter out non-rigid objects using semantic information, track the point clouds of rigid objects, reconstruct the static background, and store the resulting map in an octree format. Experimental results demonstrate that the proposed method surpasses current mainstream dynamic VSLAM techniques in both localization accuracy and real-time performance.
Related Topics
Concepts
Computer vision
Optical flow
Artificial intelligence
Computer science
Simultaneous localization and mapping
Segmentation
Point cloud
RGB color model
Robot
Image (mathematics)
Mobile robot
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s24185929
- OA Status
- gold
- Cited By
- 9
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402482649
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4402482649Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s24185929Digital Object Identifier
- Title
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DIO-SLAM: A Dynamic RGB-D SLAM Method Combining Instance Segmentation and Optical FlowWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-09-12Full publication date if available
- Authors
-
Lang He, Shiyun Li, Jun-Ting Qiu, C ZhangList of authors in order
- Landing page
-
https://doi.org/10.3390/s24185929Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/s24185929Direct OA link when available
- Concepts
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Computer vision, Optical flow, Artificial intelligence, Computer science, Simultaneous localization and mapping, Segmentation, Point cloud, RGB color model, Robot, Image (mathematics), Mobile robotTop concepts (fields/topics) attached by OpenAlex
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9Total citation count in OpenAlex
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2025: 9Per-year citation counts (last 5 years)
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49Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W4392174005, https://openalex.org/W4283166616, https://openalex.org/W4283373333, https://openalex.org/W4211154833, https://openalex.org/W4310626783, https://openalex.org/W2916733269, https://openalex.org/W2535547924, https://openalex.org/W1612997784, https://openalex.org/W3043971245, https://openalex.org/W6674952988, https://openalex.org/W2802671577, https://openalex.org/W2583491955, https://openalex.org/W2964226622, https://openalex.org/W2963150697, https://openalex.org/W2250172176, https://openalex.org/W2950989657, https://openalex.org/W2963881378, https://openalex.org/W4296280925, https://openalex.org/W6784930956, https://openalex.org/W2808571300, https://openalex.org/W3146082954, https://openalex.org/W4378189021, https://openalex.org/W4386864869, https://openalex.org/W3090298435, https://openalex.org/W2963782415, https://openalex.org/W4210347174, https://openalex.org/W4309725620, https://openalex.org/W4303981178, https://openalex.org/W2118877769, https://openalex.org/W3013130767, https://openalex.org/W4313017528, https://openalex.org/W4399154295, https://openalex.org/W6760739867, https://openalex.org/W3206778864, https://openalex.org/W4250462903, https://openalex.org/W3215023725, https://openalex.org/W3184865089, https://openalex.org/W2021851106, https://openalex.org/W3185918112, https://openalex.org/W3120564201, https://openalex.org/W4312290933, https://openalex.org/W3003179103, https://openalex.org/W2058535340, https://openalex.org/W2964156315, https://openalex.org/W3106458387, https://openalex.org/W3093600664, https://openalex.org/W3102327032, https://openalex.org/W3104842437, https://openalex.org/W4211046286 |
| referenced_works_count | 49 |
| abstract_inverted_index.a | 49, 88, 142, 150 |
| abstract_inverted_index.At | 66 |
| abstract_inverted_index.To | 36, 132 |
| abstract_inverted_index.an | 106, 181 |
| abstract_inverted_index.as | 125 |
| abstract_inverted_index.by | 99, 136 |
| abstract_inverted_index.in | 180, 197 |
| abstract_inverted_index.is | 111, 147, 154 |
| abstract_inverted_index.of | 10, 34, 96, 118, 168 |
| abstract_inverted_index.or | 20, 128, 139 |
| abstract_inverted_index.to | 27, 68, 91, 113, 156 |
| abstract_inverted_index.and | 14, 72, 86, 175, 201 |
| abstract_inverted_index.can | 5 |
| abstract_inverted_index.for | 54 |
| abstract_inverted_index.map | 179 |
| abstract_inverted_index.out | 158 |
| abstract_inverted_index.the | 8, 30, 58, 76, 79, 93, 115, 165, 172, 177, 188 |
| abstract_inverted_index.(You | 63 |
| abstract_inverted_index.Flow | 47 |
| abstract_inverted_index.Look | 65 |
| abstract_inverted_index.Only | 64 |
| abstract_inverted_index.both | 198 |
| abstract_inverted_index.fail | 26 |
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| abstract_inverted_index.them | 124 |
| abstract_inverted_index.this | 38, 40 |
| abstract_inverted_index.true | 31 |
| abstract_inverted_index.SLAM, | 48 |
| abstract_inverted_index.VSLAM | 23, 50, 195 |
| abstract_inverted_index.blur, | 141 |
| abstract_inverted_index.dense | 151 |
| abstract_inverted_index.frame | 144 |
| abstract_inverted_index.novel | 89 |
| abstract_inverted_index.often | 25 |
| abstract_inverted_index.paper | 41 |
| abstract_inverted_index.point | 166 |
| abstract_inverted_index.rigid | 71, 119, 130, 169 |
| abstract_inverted_index.state | 33 |
| abstract_inverted_index.store | 176 |
| abstract_inverted_index.this, | 105 |
| abstract_inverted_index.track | 164 |
| abstract_inverted_index.using | 161 |
| abstract_inverted_index.while | 18 |
| abstract_inverted_index.Visual | 11 |
| abstract_inverted_index.YOLACT | 62 |
| abstract_inverted_index.assess | 114 |
| abstract_inverted_index.caused | 135 |
| abstract_inverted_index.clouds | 167 |
| abstract_inverted_index.either | 126 |
| abstract_inverted_index.errors | 134 |
| abstract_inverted_index.filter | 157 |
| abstract_inverted_index.impact | 7 |
| abstract_inverted_index.method | 110, 146, 190 |
| abstract_inverted_index.missed | 137 |
| abstract_inverted_index.motion | 32, 140, 143 |
| abstract_inverted_index.moving | 3, 97, 127 |
| abstract_inverted_index.nature | 117 |
| abstract_inverted_index.object | 120 |
| abstract_inverted_index.octree | 182 |
| abstract_inverted_index.points | 1 |
| abstract_inverted_index.scene. | 77 |
| abstract_inverted_index.static | 173 |
| abstract_inverted_index.system | 51 |
| abstract_inverted_index.thread | 60, 82, 153 |
| abstract_inverted_index.within | 75 |
| abstract_inverted_index.(VSLAM) | 16 |
| abstract_inverted_index.Dynamic | 44 |
| abstract_inverted_index.Feature | 0 |
| abstract_inverted_index.Lastly, | 149 |
| abstract_inverted_index.Mapping | 15 |
| abstract_inverted_index.Optical | 46 |
| abstract_inverted_index.address | 37 |
| abstract_inverted_index.between | 70 |
| abstract_inverted_index.capture | 92 |
| abstract_inverted_index.current | 192 |
| abstract_inverted_index.dynamic | 55, 116, 194 |
| abstract_inverted_index.employs | 61 |
| abstract_inverted_index.format. | 183 |
| abstract_inverted_index.mapping | 152 |
| abstract_inverted_index.objects | 4, 74, 98, 160 |
| abstract_inverted_index.optical | 80, 84, 94, 101, 107 |
| abstract_inverted_index.results | 185 |
| abstract_inverted_index.Instance | 45 |
| abstract_inverted_index.accuracy | 9, 200 |
| abstract_inverted_index.approach | 90 |
| abstract_inverted_index.designed | 53 |
| abstract_inverted_index.mitigate | 133 |
| abstract_inverted_index.objects, | 170 |
| abstract_inverted_index.objects. | 35, 131 |
| abstract_inverted_index.proposed | 189 |
| abstract_inverted_index.regions, | 122 |
| abstract_inverted_index.semantic | 21, 162 |
| abstract_inverted_index.DIO-SLAM: | 43 |
| abstract_inverted_index.Following | 104 |
| abstract_inverted_index.detection | 19, 59 |
| abstract_inverted_index.determine | 29 |
| abstract_inverted_index.employed. | 148 |
| abstract_inverted_index.estimates | 83 |
| abstract_inverted_index.non-rigid | 73, 159 |
| abstract_inverted_index.real-time | 202 |
| abstract_inverted_index.resulting | 178 |
| abstract_inverted_index.surpasses | 191 |
| abstract_inverted_index.Initially, | 57 |
| abstract_inverted_index.accurately | 28 |
| abstract_inverted_index.approaches | 24 |
| abstract_inverted_index.challenge, | 39 |
| abstract_inverted_index.detections | 138 |
| abstract_inverted_index.introduces | 42, 87 |
| abstract_inverted_index.leveraging | 100 |
| abstract_inverted_index.mainstream | 193 |
| abstract_inverted_index.negatively | 6 |
| abstract_inverted_index.residuals. | 103 |
| abstract_inverted_index.stationary | 129 |
| abstract_inverted_index.techniques | 196 |
| abstract_inverted_index.algorithms, | 17 |
| abstract_inverted_index.background, | 174 |
| abstract_inverted_index.classifying | 123 |
| abstract_inverted_index.consistency | 109 |
| abstract_inverted_index.demonstrate | 186 |
| abstract_inverted_index.distinguish | 69 |
| abstract_inverted_index.implemented | 112 |
| abstract_inverted_index.propagation | 145 |
| abstract_inverted_index.reconstruct | 171 |
| abstract_inverted_index.Experimental | 184 |
| abstract_inverted_index.Localization | 13 |
| abstract_inverted_index.Simultaneous | 12 |
| abstract_inverted_index.incorporated | 155 |
| abstract_inverted_index.information, | 163 |
| abstract_inverted_index.localization | 199 |
| abstract_inverted_index.performance. | 203 |
| abstract_inverted_index.specifically | 52 |
| abstract_inverted_index.CoefficienTs) | 67 |
| abstract_inverted_index.Subsequently, | 78 |
| abstract_inverted_index.environments. | 56 |
| abstract_inverted_index.segmentation-based | 22 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.97653609 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |