CCTNet: A Circular Convolutional Transformer Network for LiDAR-based Place Recognition Handling Movable Objects Occlusion Article Swipe
Gang Wang
,
Chaoran Zhu
,
Qian Xu
,
Tongzhou Zhang
,
Hai Zhang
,
Xiaopeng Fan
,
Jue Hu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2405.10793
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2405.10793
Place recognition is a fundamental task for robotic application, allowing robots to perform loop closure detection within simultaneous localization and mapping (SLAM), and achieve relocalization on prior maps. Current range image-based networks use single-column convolution to maintain feature invariance to shifts in image columns caused by LiDAR viewpoint change.However, this raises the issues such as "restricted receptive fields" and "excessive focus on local regions", degrading the performance of networks. To address the aforementioned issues, we propose a lightweight circular convolutional Transformer network denoted as CCTNet, which boosts performance by capturing structural information in point clouds and facilitating crossdimensional interaction of spatial and channel information. Initially, a Circular Convolution Module (CCM) is introduced, expanding the network's perceptual field while maintaining feature consistency across varying LiDAR perspectives. Then, a Range Transformer Module (RTM) is proposed, which enhances place recognition accuracy in scenarios with movable objects by employing a combination of channel and spatial attention mechanisms. Furthermore, we propose an Overlap-based loss function, transforming the place recognition task from a binary loop closure classification into a regression problem linked to the overlap between LiDAR frames. Through extensive experiments on the KITTI and Ford Campus datasets, CCTNet surpasses comparable methods, achieving Recall@1 of 0.924 and 0.965, and Recall@1% of 0.990 and 0.993 on the test set, showcasing a superior performance. Results on the selfcollected dataset further demonstrate the proposed method's potential for practical implementation in complex scenarios to handle movable objects, showing improved generalization in various datasets.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2405.10793
- https://arxiv.org/pdf/2405.10793
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4398157351
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- OpenAlex ID
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https://openalex.org/W4398157351Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2405.10793Digital Object Identifier
- Title
-
CCTNet: A Circular Convolutional Transformer Network for LiDAR-based Place Recognition Handling Movable Objects OcclusionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-17Full publication date if available
- Authors
-
Gang Wang, Chaoran Zhu, Qian Xu, Tongzhou Zhang, Hai Zhang, Xiaopeng Fan, Jue HuList of authors in order
- Landing page
-
https://arxiv.org/abs/2405.10793Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2405.10793Direct 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/2405.10793Direct OA link when available
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Transformer, Lidar, Computer science, Artificial intelligence, Computer vision, Pattern recognition (psychology), Engineering, Geography, Remote sensing, Electrical engineering, VoltageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.achieving | 196 |
| abstract_inverted_index.attention | 151 |
| abstract_inverted_index.capturing | 89 |
| abstract_inverted_index.datasets, | 191 |
| abstract_inverted_index.datasets. | 242 |
| abstract_inverted_index.degrading | 64 |
| abstract_inverted_index.detection | 15 |
| abstract_inverted_index.employing | 144 |
| abstract_inverted_index.expanding | 112 |
| abstract_inverted_index.extensive | 183 |
| abstract_inverted_index.function, | 159 |
| abstract_inverted_index.network's | 114 |
| abstract_inverted_index.networks. | 68 |
| abstract_inverted_index.potential | 226 |
| abstract_inverted_index.practical | 228 |
| abstract_inverted_index.proposed, | 132 |
| abstract_inverted_index.receptive | 56 |
| abstract_inverted_index.regions", | 63 |
| abstract_inverted_index.scenarios | 139, 232 |
| abstract_inverted_index.surpasses | 193 |
| abstract_inverted_index.viewpoint | 47 |
| abstract_inverted_index."excessive | 59 |
| abstract_inverted_index.Initially, | 104 |
| abstract_inverted_index.comparable | 194 |
| abstract_inverted_index.invariance | 38 |
| abstract_inverted_index.perceptual | 115 |
| abstract_inverted_index.regression | 173 |
| abstract_inverted_index.showcasing | 212 |
| abstract_inverted_index.structural | 90 |
| abstract_inverted_index."restricted | 55 |
| abstract_inverted_index.Convolution | 107 |
| abstract_inverted_index.Transformer | 80, 128 |
| abstract_inverted_index.combination | 146 |
| abstract_inverted_index.consistency | 120 |
| abstract_inverted_index.convolution | 34 |
| abstract_inverted_index.demonstrate | 222 |
| abstract_inverted_index.experiments | 184 |
| abstract_inverted_index.fundamental | 4 |
| abstract_inverted_index.image-based | 30 |
| abstract_inverted_index.information | 91 |
| abstract_inverted_index.interaction | 98 |
| abstract_inverted_index.introduced, | 111 |
| abstract_inverted_index.lightweight | 77 |
| abstract_inverted_index.maintaining | 118 |
| abstract_inverted_index.mechanisms. | 152 |
| abstract_inverted_index.performance | 66, 87 |
| abstract_inverted_index.recognition | 1, 136, 163 |
| abstract_inverted_index.Furthermore, | 153 |
| abstract_inverted_index.application, | 8 |
| abstract_inverted_index.facilitating | 96 |
| abstract_inverted_index.information. | 103 |
| abstract_inverted_index.localization | 18 |
| abstract_inverted_index.performance. | 215 |
| abstract_inverted_index.simultaneous | 17 |
| abstract_inverted_index.transforming | 160 |
| abstract_inverted_index.Overlap-based | 157 |
| abstract_inverted_index.convolutional | 79 |
| abstract_inverted_index.perspectives. | 124 |
| abstract_inverted_index.selfcollected | 219 |
| abstract_inverted_index.single-column | 33 |
| abstract_inverted_index.aforementioned | 72 |
| abstract_inverted_index.classification | 170 |
| abstract_inverted_index.generalization | 239 |
| abstract_inverted_index.implementation | 229 |
| abstract_inverted_index.relocalization | 24 |
| abstract_inverted_index.change.However, | 48 |
| abstract_inverted_index.crossdimensional | 97 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |