SSC: Semantic Scan Context for Large-Scale Place Recognition

• URL: http://arxiv.org/abs/2107.00382v1
• Date: Thu, 1 Jul 2021 11:51:19 GMT
• Title: SSC: Semantic Scan Context for Large-Scale Place Recognition
• Authors: Lin Li, Xin Kong, Xiangrui Zhao, Tianxin Huang and Yong Liu
• Abstract summary: We explore the use of high-level features, namely semantics, to improve the representation ability of descriptors. We propose a novel global descriptor, Semantic Scan Context, which explores semantic information to represent scenes more effectively. Our approach outperforms the state-of-the-art methods with a large margin.
• Score: 13.228580954956342
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Place recognition gives a SLAM system the ability to correct cumulative errors. Unlike images that contain rich texture features, point clouds are almost pure geometric information which makes place recognition based on point clouds challenging. Existing works usually encode low-level features such as coordinate, normal, reflection intensity, etc., as local or global descriptors to represent scenes. Besides, they often ignore the translation between point clouds when matching descriptors. Different from most existing methods, we explore the use of high-level features, namely semantics, to improve the descriptor's representation ability. Also, when matching descriptors, we try to correct the translation between point clouds to improve accuracy. Concretely, we propose a novel global descriptor, Semantic Scan Context, which explores semantic information to represent scenes more effectively. We also present a two-step global semantic ICP to obtain the 3D pose (x, y, yaw) used to align the point cloud to improve matching performance. Our experiments on the KITTI dataset show that our approach outperforms the state-of-the-art methods with a large margin. Our code is available at: https://github.com/lilin-hitcrt/SSC.

Related papers

• FUSELOC: Fusing Global and Local Descriptors to Disambiguate 2D-3D Matching in Visual Localization [57.59857784298536]
  Direct 2D-3D matching algorithms require significantly less memory but suffer from lower accuracy due to the larger and more ambiguous search space. We address this ambiguity by fusing local and global descriptors using a weighted average operator within a 2D-3D search framework. We consistently improve the accuracy over local-only systems and achieve performance close to hierarchical methods while halving memory requirements.
  arXiv  Detail & Related papers  (2024-08-21T23:42:16Z)
• Text2Loc: 3D Point Cloud Localization from Natural Language [49.01851743372889]
  We tackle the problem of 3D point cloud localization based on a few natural linguistic descriptions. We introduce a novel neural network, Text2Loc, that fully interprets the semantic relationship between points and text. Text2Loc improves the localization accuracy by up to $2times$ over the state-of-the-art on the KITTI360Pose dataset.
  arXiv  Detail & Related papers  (2023-11-27T16:23:01Z)
• CPCM: Contextual Point Cloud Modeling for Weakly-supervised Point Cloud Semantic Segmentation [60.0893353960514]
  We study the task of weakly-supervised point cloud semantic segmentation with sparse annotations. We propose a Contextual Point Cloud Modeling ( CPCM) method that consists of two parts: a region-wise masking (RegionMask) strategy and a contextual masked training (CMT) method.
  arXiv  Detail & Related papers  (2023-07-19T04:41:18Z)
• Quadric Representations for LiDAR Odometry, Mapping and Localization [93.24140840537912]
  Current LiDAR odometry, mapping and localization methods leverage point-wise representations of 3D scenes. We propose a novel method of describing scenes using quadric surfaces, which are far more compact representations of 3D objects. Our method maintains low latency and memory utility while achieving competitive, and even superior, accuracy.
  arXiv  Detail & Related papers  (2023-04-27T13:52:01Z)
• Learning a Task-specific Descriptor for Robust Matching of 3D Point Clouds [40.81429160296275]
  We learn a robust task-specific feature descriptor to consistently describe the correct point correspondence under interference. Our method EDFNet develops from two aspects. First, we augment the matchability of correspondences by utilizing their repetitive local structure.
  arXiv  Detail & Related papers  (2022-10-26T17:57:23Z)
• DSC: Deep Scan Context Descriptor for Large-Scale Place Recognition [4.878199122477076]
  LiDAR-based place recognition is an essential and challenging task both in loop closure detection and global relocalization. We propose Deep Scan Context (DSC), a general and discriminative global descriptor that captures the relationship among segments of a point cloud.
  arXiv  Detail & Related papers  (2021-11-27T07:31:32Z)
• UPDesc: Unsupervised Point Descriptor Learning for Robust Registration [54.95201961399334]
  UPDesc is an unsupervised method to learn point descriptors for robust point cloud registration. We show that our learned descriptors yield superior performance over existing unsupervised methods.
  arXiv  Detail & Related papers  (2021-08-05T17:11:08Z)
• Robust Place Recognition using an Imaging Lidar [45.37172889338924]
  We propose a methodology for robust, real-time place recognition using an imaging lidar. Our method is truly-invariant and can tackle reverse revisiting and upside-down revisiting.
  arXiv  Detail & Related papers  (2021-03-03T01:08:31Z)
• Semantic Graph Based Place Recognition for 3D Point Clouds [22.608115489674653]
  This paper presents a novel semantic graph based approach for place recognition. First, we propose a novel semantic graph representation for the point cloud scenes. We then design a fast and effective graph similarity network to compute the similarity.
  arXiv  Detail & Related papers  (2020-08-26T09:27:26Z)
• DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization [56.15308829924527]
  We propose a Siamese network that jointly learns 3D local feature detection and description directly from raw 3D points. For detecting 3D keypoints we predict the discriminativeness of the local descriptors in an unsupervised manner. Experiments on various benchmarks demonstrate that our method achieves competitive results for both global point cloud retrieval and local point cloud registration.
  arXiv  Detail & Related papers  (2020-07-17T20:21:22Z)

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.