Real-time RGBD odometry for fused-state navigation systems Article Swipe
Andrew Willis
,
Kevin Brink
·
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.1109/plans.2016.7479744
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.1109/plans.2016.7479744
This article describes an algorithm that provides visual odometry estimates\nfrom sequential pairs of RGBD images. The key contribution of this article on\nRGBD odometry is that it provides both an odometry estimate and a covariance\nfor the odometry parameters in real-time via a representative covariance\nmatrix. Accurate, real-time parameter covariance is essential to effectively\nfuse odometry measurements into most navigation systems. To date, this topic\nhas seen little treatment in research which limits the impact existing RGBD\nodometry approaches have for localization in these systems. Covariance\nestimates are obtained via a statistical perturbation approach motivated by\nreal-world models of RGBD sensor measurement noise. Results discuss the\naccuracy of our RGBD odometry approach with respect to ground truth obtained\nfrom a motion capture system and characterizes the suitability of this approach\nfor estimating the true RGBD odometry parameter uncertainty.\n
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1109/plans.2016.7479744
- OA Status
- green
- Cited By
- 8
- References
- 23
- Related Works
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- OpenAlex ID
- https://openalex.org/W2412578866
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2412578866Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/plans.2016.7479744Digital Object Identifier
- Title
-
Real-time RGBD odometry for fused-state navigation systemsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2016Year of publication
- Publication date
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2016-04-01Full publication date if available
- Authors
-
Andrew Willis, Kevin BrinkList of authors in order
- Landing page
-
https://doi.org/10.1109/plans.2016.7479744Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2103.06236Direct OA link when available
- Concepts
-
Odometry, Visual odometry, Artificial intelligence, Covariance, Computer vision, Computer science, Covariance matrix, Noise (video), Ground truth, Algorithm, Mathematics, Robot, Mobile robot, Image (mathematics), StatisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
-
2022: 1, 2020: 3, 2018: 1, 2017: 1, 2016: 2Per-year citation counts (last 5 years)
- References (count)
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23Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.estimate | 30 |
| abstract_inverted_index.existing | 70 |
| abstract_inverted_index.obtained | 81 |
| abstract_inverted_index.odometry | 8, 22, 29, 35, 51, 101, 124 |
| abstract_inverted_index.on\nRGBD | 21 |
| abstract_inverted_index.provides | 6, 26 |
| abstract_inverted_index.research | 65 |
| abstract_inverted_index.systems. | 56, 78 |
| abstract_inverted_index.Accurate, | 43 |
| abstract_inverted_index.algorithm | 4 |
| abstract_inverted_index.describes | 2 |
| abstract_inverted_index.essential | 48 |
| abstract_inverted_index.motivated | 87 |
| abstract_inverted_index.parameter | 45, 125 |
| abstract_inverted_index.real-time | 38, 44 |
| abstract_inverted_index.treatment | 63 |
| abstract_inverted_index.approaches | 72 |
| abstract_inverted_index.covariance | 46 |
| abstract_inverted_index.estimating | 120 |
| abstract_inverted_index.navigation | 55 |
| abstract_inverted_index.parameters | 36 |
| abstract_inverted_index.sequential | 10 |
| abstract_inverted_index.topic\nhas | 60 |
| abstract_inverted_index.measurement | 93 |
| abstract_inverted_index.statistical | 84 |
| abstract_inverted_index.suitability | 116 |
| abstract_inverted_index.contribution | 17 |
| abstract_inverted_index.localization | 75 |
| abstract_inverted_index.measurements | 52 |
| abstract_inverted_index.perturbation | 85 |
| abstract_inverted_index.approach\nfor | 119 |
| abstract_inverted_index.characterizes | 114 |
| abstract_inverted_index.the\naccuracy | 97 |
| abstract_inverted_index.RGBD\nodometry | 71 |
| abstract_inverted_index.by\nreal-world | 88 |
| abstract_inverted_index.obtained\nfrom | 108 |
| abstract_inverted_index.representative | 41 |
| abstract_inverted_index.uncertainty.\n | 126 |
| abstract_inverted_index.covariance\nfor | 33 |
| abstract_inverted_index.estimates\nfrom | 9 |
| abstract_inverted_index.effectively\nfuse | 50 |
| abstract_inverted_index.covariance\nmatrix. | 42 |
| abstract_inverted_index.Covariance\nestimates | 79 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.92131552 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |