Planetary UAV localization based on Multi-modal Registration with Pre-existing Digital Terrain Model Article Swipe
Xue Wan
,
Yuanbin Shao
,
Shengyang Li
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2106.12738
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2106.12738
The autonomous real-time optical navigation of planetary UAV is of the key technologies to ensure the success of the exploration. In such a GPS denied environment, vision-based localization is an optimal approach. In this paper, we proposed a multi-modal registration based SLAM algorithm, which estimates the location of a planet UAV using a nadir view camera on the UAV compared with pre-existing digital terrain model. To overcome the scale and appearance difference between on-board UAV images and pre-installed digital terrain model, a theoretical model is proposed to prove that topographic features of UAV image and DEM can be correlated in frequency domain via cross power spectrum. To provide the six-DOF of the UAV, we also developed an optimization approach which fuses the geo-referencing result into a SLAM system via LBA (Local Bundle Adjustment) to achieve robust and accurate vision-based navigation even in featureless planetary areas. To test the robustness and effectiveness of the proposed localization algorithm, a new cross-source drone-based localization dataset for planetary exploration is proposed. The proposed dataset includes 40200 synthetic drone images taken from nine planetary scenes with related DEM query images. Comparison experiments carried out demonstrate that over the flight distance of 33.8km, the proposed method achieved average localization error of 0.45 meters, compared to 1.31 meters by ORB-SLAM, with the processing speed of 12hz which will ensure a real-time performance. We will make our datasets available to encourage further work on this promising topic.
Related Topics
Concepts
Computer science
Computer vision
Artificial intelligence
Bundle adjustment
Terrain
Robustness (evolution)
Modal
Digital elevation model
Global Positioning System
Drone
Remote sensing
Image (mathematics)
Geography
Biology
Chemistry
Cartography
Genetics
Telecommunications
Polymer chemistry
Biochemistry
Gene
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2106.12738
- https://arxiv.org/pdf/2106.12738
- OA Status
- green
- Cited By
- 3
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3173526862
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3173526862Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2106.12738Digital Object Identifier
- Title
-
Planetary UAV localization based on Multi-modal Registration with Pre-existing Digital Terrain ModelWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-06-24Full publication date if available
- Authors
-
Xue Wan, Yuanbin Shao, Shengyang LiList of authors in order
- Landing page
-
https://arxiv.org/abs/2106.12738Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2106.12738Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2106.12738Direct OA link when available
- Concepts
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Computer science, Computer vision, Artificial intelligence, Bundle adjustment, Terrain, Robustness (evolution), Modal, Digital elevation model, Global Positioning System, Drone, Remote sensing, Image (mathematics), Geography, Biology, Chemistry, Cartography, Genetics, Telecommunications, Polymer chemistry, Biochemistry, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 2, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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27Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.over | 191 |
| abstract_inverted_index.such | 21 |
| abstract_inverted_index.test | 146 |
| abstract_inverted_index.that | 88, 190 |
| abstract_inverted_index.this | 33, 236 |
| abstract_inverted_index.view | 54 |
| abstract_inverted_index.will | 220, 226 |
| abstract_inverted_index.with | 60, 180, 213 |
| abstract_inverted_index.work | 234 |
| abstract_inverted_index.40200 | 171 |
| abstract_inverted_index.based | 40 |
| abstract_inverted_index.cross | 103 |
| abstract_inverted_index.drone | 173 |
| abstract_inverted_index.error | 203 |
| abstract_inverted_index.fuses | 120 |
| abstract_inverted_index.image | 93 |
| abstract_inverted_index.model | 83 |
| abstract_inverted_index.nadir | 53 |
| abstract_inverted_index.power | 104 |
| abstract_inverted_index.prove | 87 |
| abstract_inverted_index.query | 183 |
| abstract_inverted_index.scale | 68 |
| abstract_inverted_index.speed | 216 |
| abstract_inverted_index.taken | 175 |
| abstract_inverted_index.using | 51 |
| abstract_inverted_index.which | 43, 119, 219 |
| abstract_inverted_index.(Local | 130 |
| abstract_inverted_index.Bundle | 131 |
| abstract_inverted_index.areas. | 144 |
| abstract_inverted_index.camera | 55 |
| abstract_inverted_index.denied | 24 |
| abstract_inverted_index.domain | 101 |
| abstract_inverted_index.ensure | 14, 221 |
| abstract_inverted_index.flight | 193 |
| abstract_inverted_index.images | 75, 174 |
| abstract_inverted_index.meters | 210 |
| abstract_inverted_index.method | 199 |
| abstract_inverted_index.model, | 80 |
| abstract_inverted_index.model. | 64 |
| abstract_inverted_index.paper, | 34 |
| abstract_inverted_index.planet | 49 |
| abstract_inverted_index.result | 123 |
| abstract_inverted_index.robust | 135 |
| abstract_inverted_index.scenes | 179 |
| abstract_inverted_index.system | 127 |
| abstract_inverted_index.topic. | 238 |
| abstract_inverted_index.33.8km, | 196 |
| abstract_inverted_index.achieve | 134 |
| abstract_inverted_index.average | 201 |
| abstract_inverted_index.between | 72 |
| abstract_inverted_index.carried | 187 |
| abstract_inverted_index.dataset | 161, 169 |
| abstract_inverted_index.digital | 62, 78 |
| abstract_inverted_index.further | 233 |
| abstract_inverted_index.images. | 184 |
| abstract_inverted_index.meters, | 206 |
| abstract_inverted_index.optical | 3 |
| abstract_inverted_index.optimal | 30 |
| abstract_inverted_index.provide | 107 |
| abstract_inverted_index.related | 181 |
| abstract_inverted_index.six-DOF | 109 |
| abstract_inverted_index.success | 16 |
| abstract_inverted_index.terrain | 63, 79 |
| abstract_inverted_index.accurate | 137 |
| abstract_inverted_index.achieved | 200 |
| abstract_inverted_index.approach | 118 |
| abstract_inverted_index.compared | 59, 207 |
| abstract_inverted_index.datasets | 229 |
| abstract_inverted_index.distance | 194 |
| abstract_inverted_index.features | 90 |
| abstract_inverted_index.includes | 170 |
| abstract_inverted_index.location | 46 |
| abstract_inverted_index.on-board | 73 |
| abstract_inverted_index.overcome | 66 |
| abstract_inverted_index.proposed | 36, 85, 153, 168, 198 |
| abstract_inverted_index.ORB-SLAM, | 212 |
| abstract_inverted_index.approach. | 31 |
| abstract_inverted_index.available | 230 |
| abstract_inverted_index.developed | 115 |
| abstract_inverted_index.encourage | 232 |
| abstract_inverted_index.estimates | 44 |
| abstract_inverted_index.frequency | 100 |
| abstract_inverted_index.planetary | 6, 143, 163, 178 |
| abstract_inverted_index.promising | 237 |
| abstract_inverted_index.proposed. | 166 |
| abstract_inverted_index.real-time | 2, 223 |
| abstract_inverted_index.spectrum. | 105 |
| abstract_inverted_index.synthetic | 172 |
| abstract_inverted_index.Comparison | 185 |
| abstract_inverted_index.algorithm, | 42, 155 |
| abstract_inverted_index.appearance | 70 |
| abstract_inverted_index.autonomous | 1 |
| abstract_inverted_index.correlated | 98 |
| abstract_inverted_index.difference | 71 |
| abstract_inverted_index.navigation | 4, 139 |
| abstract_inverted_index.processing | 215 |
| abstract_inverted_index.robustness | 148 |
| abstract_inverted_index.Adjustment) | 132 |
| abstract_inverted_index.demonstrate | 189 |
| abstract_inverted_index.drone-based | 159 |
| abstract_inverted_index.experiments | 186 |
| abstract_inverted_index.exploration | 164 |
| abstract_inverted_index.featureless | 142 |
| abstract_inverted_index.multi-modal | 38 |
| abstract_inverted_index.theoretical | 82 |
| abstract_inverted_index.topographic | 89 |
| abstract_inverted_index.cross-source | 158 |
| abstract_inverted_index.environment, | 25 |
| abstract_inverted_index.exploration. | 19 |
| abstract_inverted_index.localization | 27, 154, 160, 202 |
| abstract_inverted_index.optimization | 117 |
| abstract_inverted_index.performance. | 224 |
| abstract_inverted_index.pre-existing | 61 |
| abstract_inverted_index.registration | 39 |
| abstract_inverted_index.technologies | 12 |
| abstract_inverted_index.vision-based | 26, 138 |
| abstract_inverted_index.effectiveness | 150 |
| abstract_inverted_index.pre-installed | 77 |
| abstract_inverted_index.geo-referencing | 122 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |