The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Integrating metric and semantic maps for vision-only automated parking
Abstract
We present a framework for integrating two layers of map which are often required for fully automated operation: metric and semantic. Metric maps are likely to improve with subsequent visitations to the same place, while semantic maps can comprise both permanent and fluctuating features of the environment. However, it is not clear from the state of the art how to update the semantic layer as the metric map evolves. The strengths of our method are threefold: the framework allows for the unsupervised evolution of both maps as the environment is revisited by the robot; it uses vision-only sensors, making it appropriate for production cars; and the human labelling effort is minimised as far as possible while maintaining high fidelity. We evaluate this on two different car parks with a fully automated car, performing repeated automated parking manoeuvres to demonstrate the robustness of the system.
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Most datasets have focused on 2D annotations (boxes, masks) for RGB camera images.
“Most datasets have focused on 2D annotations (boxes, masks) for RGB camera images.”
From nuScenes: A Multimodal Dataset for Autonomous Driving (2020) · cites this paper as [19]
Most datasets provide 2D semantic annotations as boxes or masks.
“Most datasets provide 2D semantic annotations as boxes or masks (class or instance) [8, 19, 33, 85, 55].”
From nuScenes: A Multimodal Dataset for Autonomous Driving (2020) · cites this paper as [19]
CamVid, Cityscapes, Mapillary Vistas, D2-City, BDD100k and Apolloscape released ever growing datasets with segmentation masks.
“CamVid [8], Cityscapes [19], Mapillary Vistas [33], D 2 -City [11], BDD100k [85] and Apolloscape [41] released ever growing datasets with segmentation masks.”
From nuScenes: A Multimodal Dataset for Autonomous Driving (2020) · cites this paper as [19]
Contextual knowledge using semantic maps is an important prior for scene understanding.
“Contextual knowledge using semantic maps is also an important prior for scene understanding [82, 2, 35].”
From nuScenes: A Multimodal Dataset for Autonomous Driving (2020) · cites this paper as [35]
mAP with a threshold on IOU is perhaps the most popular metric for object detection.
“mAP with a threshold on IOU is perhaps the most popular metric for object detection [32, 19, 21].”
From nuScenes: A Multimodal Dataset for Autonomous Driving (2020) · cites this paper as [19]
Most existing datasets provide the vehicle location based on GPS and IMU.
“Most existing datasets provide the vehicle location based on GPS and IMU [32, 41, 19, 61].”
From nuScenes: A Multimodal Dataset for Autonomous Driving (2020) · cites this paper as [19]
Citations
Cites
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Flagged first, then by citations
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Sorted by citations
nuScenes: A Multimodal Dataset for Autonomous Driving
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., & Beijbom, O. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01164 · Cited by 7,152
“Contextual knowledge using semantic maps is also an important prior for scene understanding [82, 2, 35].”
BackgroundSimultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving
Bresson, G., Alsayed, Z., Yu, L., & Glaser, S. (2017) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2017.2749181 · Cited by 885
Metadata
- Authors
- Grimmett, H., Buerki, M., Paz, L., Pinies, P., Furgale, P., Posner, I., & Newman, P.
- Affiliations
- University of Oxford (GB)
- ETH Zurich (CH)
- Venue
- 2015 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2015-05
- Pages
- 2159-2166
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W1588996594
- Cited by
- 29
- References
- 1
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 1 · 2021: 6 · 2020: 5 · 2019: 4 · 2018: 2 · 2017: 4 · 2016: 3 · 2015: 1
- FWCI
- 5.78
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Automated Road and Building Extraction
- Keywords
- autonomous parking, metric maps, semantic maps, map integration, map updating, fully automated vehicles, parking lots
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