LCDNet: Deep Loop Closure Detection for LiDAR SLAM based on Unbalanced Optimal Transport

• URL: http://arxiv.org/abs/2103.05056v1
• Date: Mon, 8 Mar 2021 20:19:37 GMT
• Title: LCDNet: Deep Loop Closure Detection for LiDAR SLAM based on Unbalanced Optimal Transport
• Authors: Daniele Cattaneo, Matteo Vaghi, Abhinav Valada
• Abstract summary: We introduce the novel LCDNet that effectively detects loop closures in LiDAR point clouds. LCDNet is composed of a shared encoder, a place recognition head that extracts global descriptors, and a relative pose head that estimates the transformation between two point clouds. Our approach outperforms state-of-the-art techniques by a large margin even while dealing with reverse loops.
• Score: 8.21384946488751
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Loop closure detection is an essential component of Simultaneous Localization and Mapping (SLAM) systems, which reduces the drift accumulated over time. Over the years, several deep learning approaches have been proposed to address this task, however their performance has been subpar compared to handcrafted techniques, especially while dealing with reverse loops. In this paper, we introduce the novel LCDNet that effectively detects loop closures in LiDAR point clouds by simultaneously identifying previously visited places and estimating the 6-DoF relative transformation between the current scan and the map. LCDNet is composed of a shared encoder, a place recognition head that extracts global descriptors, and a relative pose head that estimates the transformation between two point clouds. We introduce a novel relative pose head based on the unbalanced optimal transport theory that we implement in a differentiable manner to allow for end-to-end training. Extensive evaluations of LCDNet on multiple real-world autonomous driving datasets show that our approach outperforms state-of-the-art techniques by a large margin even while dealing with reverse loops. Moreover, we integrate our proposed loop closure detection approach into a LiDAR SLAM library to provide a complete mapping system and demonstrate the generalization ability using different sensor setup in an unseen city.

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)
• ELGC-Net: Efficient Local-Global Context Aggregation for Remote Sensing Change Detection [65.59969454655996]
  We propose an efficient change detection framework, ELGC-Net, which leverages rich contextual information to precisely estimate change regions. Our proposed ELGC-Net sets a new state-of-the-art performance in remote sensing change detection benchmarks. We also introduce ELGC-Net-LW, a lighter variant with significantly reduced computational complexity, suitable for resource-constrained settings.
  arXiv  Detail & Related papers  (2024-03-26T17:46:25Z)
• 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)
• Loop closure detection using local 3D deep descriptors [22.93552565583209]
  We present a method to address loop closure detection in simultaneous localisation and mapping using local 3D deep descriptors (L3Ds) We propose a novel overlap measure for loop detection by computing the metric error between points that correspond to mutually-nearest-neighbour descriptors. This novel approach enables us to accurately detect loops and estimate six degrees-of-freedom poses in the case of small overlaps.
  arXiv  Detail & Related papers  (2021-10-31T09:18:38Z)
• 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)
• Deep Shells: Unsupervised Shape Correspondence with Optimal Transport [52.646396621449]
  We propose a novel unsupervised learning approach to 3D shape correspondence. We show that the proposed method significantly improves over the state-of-the-art on multiple datasets.
  arXiv  Detail & Related papers  (2020-10-28T22:24:07Z)
• Robust Odometry and Mapping for Multi-LiDAR Systems with Online Extrinsic Calibration [15.946728828122385]
  This paper proposes a system to achieve robust and simultaneous extrinsic calibration, odometry, and mapping for multiple LiDARs. We validate our approach's performance with extensive experiments on ten sequences (4.60km total length) for the calibration and SLAM. We demonstrate that the proposed work is a complete, robust, and system for various multi-LiDAR setups.
  arXiv  Detail & Related papers  (2020-10-27T13:51:26Z)
• Anchor-free Small-scale Multispectral Pedestrian Detection [88.7497134369344]
  We propose a method for effective and efficient multispectral fusion of the two modalities in an adapted single-stage anchor-free base architecture. We aim at learning pedestrian representations based on object center and scale rather than direct bounding box predictions. Results show our method's effectiveness in detecting small-scaled pedestrians.
  arXiv  Detail & Related papers  (2020-08-19T13:13:01Z)
• A Novel CNN-based Method for Accurate Ship Detection in HR Optical Remote Sensing Images via Rotated Bounding Box [10.689750889854269]
  A novel CNN-based ship detection method is proposed, by overcoming some common deficiencies of current CNN-based methods in ship detection. We are able to predict the orientation and other variables independently, and yet more effectively, with a novel dual-branch regression network. Experimental results demonstrate the great superiority of the proposed method in ship detection.
  arXiv  Detail & Related papers  (2020-04-15T14:48:46Z)
• A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems [82.2513703281725]
  Federated learning is a privacy-preserving approach to train a global model at a central server by collaborating with wireless devices. We present a compressive sensing approach for federated learning over massive multiple-input multiple-output communication systems.
  arXiv  Detail & Related papers  (2020-03-18T05:56:27Z)
• Online LiDAR-SLAM for Legged Robots with Robust Registration and Deep-Learned Loop Closure [7.861777781616249]
  We present a factor-graph LiDAR-SLAM system which incorporates a state-of-the-art deeply learned feature-based loop closure detector. Our system uses only LiDAR sensing and was developed to run on the quadruped robot's navigation PC.
  arXiv  Detail & Related papers  (2020-01-28T10:30:20Z)

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.