DeepRING: Learning Roto-translation Invariant Representation for LiDAR based Place Recognition

• URL: http://arxiv.org/abs/2210.11029v1
• Date: Thu, 20 Oct 2022 05:35:30 GMT
• Title: DeepRING: Learning Roto-translation Invariant Representation for LiDAR based Place Recognition
• Authors: Sha Lu, Xuecheng Xu, Li Tang, Rong Xiong and Yue Wang
• Abstract summary: We propose DeepRING to learn the roto-translation invariant representation from LiDAR scan. There are two keys in DeepRING: the feature is extracted from sinogram, and the feature is aggregated by magnitude spectrum. We state the place recognition as a one-shot learning problem with each place being a class, leveraging relation learning to build representation similarity.
• Score: 12.708391665878844
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: LiDAR based place recognition is popular for loop closure detection and re-localization. In recent years, deep learning brings improvements to place recognition by learnable feature extraction. However, these methods degenerate when the robot re-visits previous places with large perspective difference. To address the challenge, we propose DeepRING to learn the roto-translation invariant representation from LiDAR scan, so that robot visits the same place with different perspective can have similar representations. There are two keys in DeepRING: the feature is extracted from sinogram, and the feature is aggregated by magnitude spectrum. The two steps keeps the final representation with both discrimination and roto-translation invariance. Moreover, we state the place recognition as a one-shot learning problem with each place being a class, leveraging relation learning to build representation similarity. Substantial experiments are carried out on public datasets, validating the effectiveness of each proposed component, and showing that DeepRING outperforms the comparative methods, especially in dataset level generalization.

Related papers

• RING#: PR-by-PE Global Localization with Roto-translation Equivariant Gram Learning [20.688641105430467]
  Global localization is crucial in autonomous driving and robotics applications when GPS signals are unreliable. Most approaches achieve global localization by sequential place recognition (PR) and pose estimation (PE) We introduce a new paradigm, PR-by-PE localization, which bypasses the need for separate place recognition by directly deriving it from pose estimation. We propose RING#, an end-to-end PR-by-PE localization network that operates in the bird's-eye-view (BEV) space, compatible with both vision and LiDAR sensors.
  arXiv  Detail & Related papers  (2024-08-30T18:42:53Z)
• Deep Homography Estimation for Visual Place Recognition [49.235432979736395]
  We propose a transformer-based deep homography estimation (DHE) network. It takes the dense feature map extracted by a backbone network as input and fits homography for fast and learnable geometric verification. Experiments on benchmark datasets show that our method can outperform several state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-02-25T13:22:17Z)
• Distillation-guided Representation Learning for Unconstrained Gait Recognition [50.0533243584942]
  We propose a framework, termed GAit DEtection and Recognition (GADER), for human authentication in challenging outdoor scenarios. GADER builds discriminative features through a novel gait recognition method, where only frames containing gait information are used. We evaluate our method on multiple State-of-The-Arts(SoTA) gait baselines and demonstrate consistent improvements on indoor and outdoor datasets.
  arXiv  Detail & Related papers  (2023-07-27T01:53:57Z)
• RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search [51.09723403468361]
  We propose a Relation and Sensitivity aware representation learning method (RaSa) RaSa includes two novel tasks: Relation-Aware learning (RA) and Sensitivity-Aware learning (SA) Experiments demonstrate that RaSa outperforms existing state-of-the-art methods by 6.94%, 4.45% and 15.35% in terms of Rank@1 on datasets.
  arXiv  Detail & Related papers  (2023-05-23T03:53:57Z)
• Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems [61.11799513362704]
  We propose learning an additional screening mechanism to identify discriminative clues commonly seen across instances and classes. We show that a common rationale detector can be learned by simply exploiting the GradCAM induced from the SSL objective.
  arXiv  Detail & Related papers  (2023-03-03T02:07:40Z)
• Unsupervised Pretraining for Object Detection by Patch Reidentification [72.75287435882798]
  Unsupervised representation learning achieves promising performances in pre-training representations for object detectors. This work proposes a simple yet effective representation learning method for object detection, named patch re-identification (Re-ID) Our method significantly outperforms its counterparts on COCO in all settings, such as different training iterations and data percentages.
  arXiv  Detail & Related papers  (2021-03-08T15:13:59Z)
• DiSCO: Differentiable Scan Context with Orientation [13.797651328615347]
  We propose a LiDAR-based place recognition method, named Differentiable Scan Context with Orientation (DiSCO) It simultaneously finds the scan at a similar place and estimates their relative orientation. DiSCO is validated on three datasets with long-term outdoor conditions.
  arXiv  Detail & Related papers  (2020-10-21T12:38:21Z)
• Gait Recognition using Multi-Scale Partial Representation Transformation with Capsules [22.99694601595627]
  We propose a novel deep network, learning to transfer multi-scale partial gait representations using capsules. Our network first obtains multi-scale partial representations using a state-of-the-art deep partial feature extractor. It then recurrently learns the correlations and co-occurrences of the patterns among the partial features in forward and backward directions.
  arXiv  Detail & Related papers  (2020-10-18T19:47:38Z)
• Distilling Localization for Self-Supervised Representation Learning [82.79808902674282]
  Contrastive learning has revolutionized unsupervised representation learning. Current contrastive models are ineffective at localizing the foreground object. We propose a data-driven approach for learning in variance to backgrounds.
  arXiv  Detail & Related papers  (2020-04-14T16:29:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.