SOLVR: Submap Oriented LiDAR-Visual Re-Localisation

• URL: http://arxiv.org/abs/2409.10247v1
• Date: Mon, 16 Sep 2024 12:58:03 GMT
• Title: SOLVR: Submap Oriented LiDAR-Visual Re-Localisation
• Authors: Joshua Knights, Sebastián Barbas Laina, Peyman Moghadam, Stefan Leutenegger,
• Abstract summary: SOLVR performs place recognition and 6-DoF registration across sensor modalities. We show that SOLVR achieves state-of-the-art performance for LiDAR-Visual place recognition and registration.
• Score: 13.434340164323473
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes SOLVR, a unified pipeline for learning based LiDAR-Visual re-localisation which performs place recognition and 6-DoF registration across sensor modalities. We propose a strategy to align the input sensor modalities by leveraging stereo image streams to produce metric depth predictions with pose information, followed by fusing multiple scene views from a local window using a probabilistic occupancy framework to expand the limited field-of-view of the camera. Additionally, SOLVR adopts a flexible definition of what constitutes positive examples for different training losses, allowing us to simultaneously optimise place recognition and registration performance. Furthermore, we replace RANSAC with a registration function that weights a simple least-squares fitting with the estimated inlier likelihood of sparse keypoint correspondences, improving performance in scenarios with a low inlier ratio between the query and retrieved place. Our experiments on the KITTI and KITTI360 datasets show that SOLVR achieves state-of-the-art performance for LiDAR-Visual place recognition and registration, particularly improving registration accuracy over larger distances between the query and retrieved place.

Related papers

• OverlapMamba: Novel Shift State Space Model for LiDAR-based Place Recognition [10.39935021754015]
  We develop OverlapMamba, a novel network for place recognition as sequences. Our method effectively detects loop closures showing even when traversing previously visited locations from different directions. Relying on raw range view inputs, it outperforms typical LiDAR and multi-view combination methods in time complexity and speed.
  arXiv  Detail & Related papers  (2024-05-13T17:46:35Z)
• Local positional graphs and attentive local features for a data and runtime-efficient hierarchical place recognition pipeline [11.099588962062937]
  This paper proposes a runtime and data-efficient hierarchical VPR pipeline that extends existing approaches and presents novel ideas. First, we propose Local Positional Graphs (LPG), a training-free and runtime-efficient approach to encode spatial context information of local image features. Second, we present Attentive Local SPED (ATLAS), an extension of our previous local features approach with an attention module. Third, we present a hierarchical pipeline that exploits hyperdimensional computing to use the same local features as holistic HDC-descriptors for fast candidate selection and for candidate reranking.
  arXiv  Detail & Related papers  (2024-03-15T13:26:39Z)
• SVRDA: A Web-based Dataset Annotation Tool for Slice-to-Volume Registration [42.03033994348234]
  The proposed tool, named SVRDA, is an installation-free web application for platform-agnostic collaborative dataset annotation. It enables efficient transformation manipulation via keyboard shortcuts and smooth case transitions with auto-saving. Various supplementary features have been implemented to facilitate slice-to-volume registration.
  arXiv  Detail & Related papers  (2023-11-27T04:49:24Z)
• UnLoc: A Universal Localization Method for Autonomous Vehicles using LiDAR, Radar and/or Camera Input [51.150605800173366]
  UnLoc is a novel unified neural modeling approach for localization with multi-sensor input in all weather conditions. Our method is extensively evaluated on Oxford Radar RobotCar, ApolloSouthBay and Perth-WA datasets.
  arXiv  Detail & Related papers  (2023-07-03T04:10:55Z)
• TReR: A Lightweight Transformer Re-Ranking Approach for 3D LiDAR Place Recognition [2.6619797838632966]
  3D LiDAR-based localization methods have used retrieval-based place recognition to find revisited places efficiently. This work tackles this problem from an information-retrieval perspective, adopting a first-retrieve-then-re-ranking paradigm. The proposed approach relies on global descriptors only, being agnostic to the place recognition model.
  arXiv  Detail & Related papers  (2023-05-29T11:10:38Z)
• MV-JAR: Masked Voxel Jigsaw and Reconstruction for LiDAR-Based Self-Supervised Pre-Training [58.07391711548269]
  Masked Voxel Jigsaw and Reconstruction (MV-JAR) method for LiDAR-based self-supervised pre-training. Masked Voxel Jigsaw and Reconstruction (MV-JAR) method for LiDAR-based self-supervised pre-training.
  arXiv  Detail & Related papers  (2023-03-23T17:59:02Z)
• Spatial Likelihood Voting with Self-Knowledge Distillation for Weakly Supervised Object Detection [54.24966006457756]
  We propose a WSOD framework called the Spatial Likelihood Voting with Self-knowledge Distillation Network (SLV-SD Net) SLV-SD Net converges region proposal localization without bounding box annotations. Experiments on the PASCAL VOC 2007/2012 and MS-COCO datasets demonstrate the excellent performance of SLV-SD Net.
  arXiv  Detail & Related papers  (2022-04-14T11:56:19Z)
• iSDF: Real-Time Neural Signed Distance Fields for Robot Perception [64.80458128766254]
  iSDF is a continuous learning system for real-time signed distance field reconstruction. It produces more accurate reconstructions and better approximations of collision costs and gradients.
  arXiv  Detail & Related papers  (2022-04-05T15:48:39Z)
• Improving Perception via Sensor Placement: Designing Multi-LiDAR Systems for Autonomous Vehicles [16.45799795374353]
  We propose an easy-to-compute information-theoretic surrogate cost metric based on Probabilistic Occupancy Grids (POG) to optimize LiDAR placement for maximal sensing. Our results confirm that sensor placement is an important factor in 3D point cloud-based object detection and could lead to a variation of performance by 10% 20% on the state-of-the-art perception algorithms.
  arXiv  Detail & Related papers  (2021-05-02T01:52:18Z)
• Inter-class Discrepancy Alignment for Face Recognition [55.578063356210144]
  We propose a unified framework calledInter-class DiscrepancyAlignment(IDA) IDA-DAO is used to align the similarity scores considering the discrepancy between the images and its neighbors. IDA-SSE can provide convincing inter-class neighbors by introducing virtual candidate images generated with GAN.
  arXiv  Detail & Related papers  (2021-03-02T08:20:08Z)
• Dense Label Encoding for Boundary Discontinuity Free Rotation Detection [69.75559390700887]
  This paper explores a relatively less-studied methodology based on classification. We propose new techniques to push its frontier in two aspects. Experiments and visual analysis on large-scale public datasets for aerial images show the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-11-19T05:42:02Z)

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.