That's My Point: Compact Object-centric LiDAR Pose Estimation for Large-scale Outdoor Localisation Article Swipe
Georgi Pramatarov
,
Matthew Gadd
,
Paul A. Newman
,
Daniele De Martini
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2403.04755
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2403.04755
This paper is about 3D pose estimation on LiDAR scans with extremely minimal storage requirements to enable scalable mapping and localisation. We achieve this by clustering all points of segmented scans into semantic objects and representing them only with their respective centroid and semantic class. In this way, each LiDAR scan is reduced to a compact collection of four-number vectors. This abstracts away important structural information from the scenes, which is crucial for traditional registration approaches. To mitigate this, we introduce an object-matching network based on self- and cross-correlation that captures geometric and semantic relationships between entities. The respective matches allow us to recover the relative transformation between scans through weighted Singular Value Decomposition (SVD) and RANdom SAmple Consensus (RANSAC). We demonstrate that such representation is sufficient for metric localisation by registering point clouds taken under different viewpoints on the KITTI dataset, and at different periods of time localising between KITTI and KITTI-360. We achieve accurate metric estimates comparable with state-of-the-art methods with almost half the representation size, specifically 1.33 kB on average.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2403.04755
- https://arxiv.org/pdf/2403.04755
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392617700
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392617700Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2403.04755Digital Object Identifier
- Title
-
That's My Point: Compact Object-centric LiDAR Pose Estimation for Large-scale Outdoor LocalisationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-03-07Full publication date if available
- Authors
-
Georgi Pramatarov, Matthew Gadd, Paul A. Newman, Daniele De MartiniList of authors in order
- Landing page
-
https://arxiv.org/abs/2403.04755Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2403.04755Direct 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/2403.04755Direct OA link when available
- Concepts
-
Lidar, Scale (ratio), Computer vision, Object (grammar), Pose, Computer science, Remote sensing, Artificial intelligence, Point (geometry), Object based, Estimation, Geography, Cartography, Engineering, Mathematics, Geometry, Systems engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.scalable | 17 |
| abstract_inverted_index.semantic | 32, 43, 93 |
| abstract_inverted_index.vectors. | 59 |
| abstract_inverted_index.weighted | 110 |
| abstract_inverted_index.(RANSAC). | 119 |
| abstract_inverted_index.Consensus | 118 |
| abstract_inverted_index.abstracts | 61 |
| abstract_inverted_index.different | 136, 144 |
| abstract_inverted_index.entities. | 96 |
| abstract_inverted_index.estimates | 157 |
| abstract_inverted_index.extremely | 11 |
| abstract_inverted_index.geometric | 91 |
| abstract_inverted_index.important | 63 |
| abstract_inverted_index.introduce | 80 |
| abstract_inverted_index.segmented | 29 |
| abstract_inverted_index.KITTI-360. | 152 |
| abstract_inverted_index.clustering | 25 |
| abstract_inverted_index.collection | 56 |
| abstract_inverted_index.comparable | 158 |
| abstract_inverted_index.estimation | 6 |
| abstract_inverted_index.localising | 148 |
| abstract_inverted_index.respective | 40, 98 |
| abstract_inverted_index.structural | 64 |
| abstract_inverted_index.sufficient | 126 |
| abstract_inverted_index.viewpoints | 137 |
| abstract_inverted_index.approaches. | 75 |
| abstract_inverted_index.demonstrate | 121 |
| abstract_inverted_index.four-number | 58 |
| abstract_inverted_index.information | 65 |
| abstract_inverted_index.registering | 131 |
| abstract_inverted_index.traditional | 73 |
| abstract_inverted_index.localisation | 129 |
| abstract_inverted_index.registration | 74 |
| abstract_inverted_index.representing | 35 |
| abstract_inverted_index.requirements | 14 |
| abstract_inverted_index.specifically | 168 |
| abstract_inverted_index.Decomposition | 113 |
| abstract_inverted_index.localisation. | 20 |
| abstract_inverted_index.relationships | 94 |
| abstract_inverted_index.representation | 124, 166 |
| abstract_inverted_index.transformation | 106 |
| abstract_inverted_index.object-matching | 82 |
| abstract_inverted_index.state-of-the-art | 160 |
| abstract_inverted_index.cross-correlation | 88 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |