A Robot Localization Framework Using CNNs for Object Detection and Pose\n Estimation Article Swipe
Lukas Hoyer
,
Christoph Steup
,
Sanaz Mostaghim
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1810.01665
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1810.01665
External localization is an essential part for the indoor operation of small\nor cost-efficient robots, as they are used, for example, in swarm robotics. We\nintroduce a two-stage localization and instance identification framework for\narbitrary robots based on convolutional neural networks. Object detection is\nperformed on an external camera image of the operation zone providing robot\nbounding boxes for an identification and orientation estimation convolutional\nneural network. Additionally, we propose a process to generate the necessary\ntraining data. The framework was evaluated with 3 different robot types and\nvarious identification patterns. We have analyzed the main framework\nhyperparameters providing recommendations for the framework operation settings.\nWe achieved up to 98% [email protected] and only 1.6{\\deg} orientation error, running\nwith a frame rate of 50 Hz on a GPU.\n
Related Topics
Concepts
Artificial intelligence
Convolutional neural network
Computer science
Pose
Robot
Bounding overwatch
Orientation (vector space)
Computer vision
Process (computing)
Hyperparameter
Object (grammar)
Object detection
Identification (biology)
Frame rate
Swarm robotics
Minimum bounding box
Frame (networking)
Pattern recognition (psychology)
Image (mathematics)
Mathematics
Biology
Geometry
Botany
Operating system
Telecommunications
Metadata
- Type
- preprint
- Landing Page
- http://arxiv.org/abs/1810.01665
- https://arxiv.org/pdf/1810.01665
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4289438529
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4289438529Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1810.01665Digital Object Identifier
- Title
-
A Robot Localization Framework Using CNNs for Object Detection and Pose\n EstimationWork title
- Type
-
preprintOpenAlex work type
- Publication year
-
2018Year of publication
- Publication date
-
2018-10-03Full publication date if available
- Authors
-
Lukas Hoyer, Christoph Steup, Sanaz MostaghimList of authors in order
- Landing page
-
https://arxiv.org/abs/1810.01665Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/1810.01665Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1810.01665Direct OA link when available
- Concepts
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Artificial intelligence, Convolutional neural network, Computer science, Pose, Robot, Bounding overwatch, Orientation (vector space), Computer vision, Process (computing), Hyperparameter, Object (grammar), Object detection, Identification (biology), Frame rate, Swarm robotics, Minimum bounding box, Frame (networking), Pattern recognition (psychology), Image (mathematics), Mathematics, Biology, Geometry, Botany, Operating system, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.detection | 39 |
| abstract_inverted_index.different | 77 |
| abstract_inverted_index.essential | 4 |
| abstract_inverted_index.evaluated | 74 |
| abstract_inverted_index.framework | 30, 72, 93 |
| abstract_inverted_index.networks. | 37 |
| abstract_inverted_index.operation | 9, 48, 94 |
| abstract_inverted_index.patterns. | 82 |
| abstract_inverted_index.providing | 50, 89 |
| abstract_inverted_index.robotics. | 22 |
| abstract_inverted_index.small\nor | 11 |
| abstract_inverted_index.two-stage | 25 |
| abstract_inverted_index.1.6{\\deg} | 103 |
| abstract_inverted_index.estimation | 58 |
| [email protected] | 100 |
| abstract_inverted_index.orientation | 57, 104 |
| abstract_inverted_index.and\nvarious | 80 |
| abstract_inverted_index.localization | 1, 26 |
| abstract_inverted_index.Additionally, | 61 |
| abstract_inverted_index.We\nintroduce | 23 |
| abstract_inverted_index.convolutional | 35 |
| abstract_inverted_index.is\nperformed | 40 |
| abstract_inverted_index.running\nwith | 106 |
| abstract_inverted_index.settings.\nWe | 95 |
| abstract_inverted_index.cost-efficient | 12 |
| abstract_inverted_index.for\narbitrary | 31 |
| abstract_inverted_index.identification | 29, 55, 81 |
| abstract_inverted_index.recommendations | 90 |
| abstract_inverted_index.robot\nbounding | 51 |
| abstract_inverted_index.necessary\ntraining | 69 |
| abstract_inverted_index.convolutional\nneural | 59 |
| abstract_inverted_index.framework\nhyperparameters | 88 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.19120368 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |