SA-LOAM: Semantic-aided LiDAR SLAM with Loop Closure

• URL: http://arxiv.org/abs/2106.11516v1
• Date: Tue, 22 Jun 2021 03:14:20 GMT
• Title: SA-LOAM: Semantic-aided LiDAR SLAM with Loop Closure
• Authors: Lin Li, Xin Kong, Xiangrui Zhao, Wanlong Li, Feng Wen, Hongbo Zhang and Yong Liu
• Abstract summary: We present a novel semantic-aided LiDAR SLAM with loop closure based on LOAM, named SA-LOAM. We can improve the localization accuracy, detect loop closures effectively, and construct a global consistent semantic map even in large-scale scenes.
• Score: 12.970919078106634
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LiDAR-based SLAM system is admittedly more accurate and stable than others, while its loop closure detection is still an open issue. With the development of 3D semantic segmentation for point cloud, semantic information can be obtained conveniently and steadily, essential for high-level intelligence and conductive to SLAM. In this paper, we present a novel semantic-aided LiDAR SLAM with loop closure based on LOAM, named SA-LOAM, which leverages semantics in odometry as well as loop closure detection. Specifically, we propose a semantic-assisted ICP, including semantically matching, downsampling and plane constraint, and integrates a semantic graph-based place recognition method in our loop closure detection module. Benefitting from semantics, we can improve the localization accuracy, detect loop closures effectively, and construct a global consistent semantic map even in large-scale scenes. Extensive experiments on KITTI and Ford Campus dataset show that our system significantly improves baseline performance, has generalization ability to unseen data and achieves competitive results compared with state-of-the-art methods.

Related papers

• SLAck: Semantic, Location, and Appearance Aware Open-Vocabulary Tracking [89.43370214059955]
  Open-vocabulary Multiple Object Tracking (MOT) aims to generalize trackers to novel categories not in the training set. We present a unified framework that jointly considers semantics, location, and appearance priors in the early steps of association. Our method eliminates complex post-processings for fusing different cues and boosts the association performance significantly for large-scale open-vocabulary tracking.
  arXiv  Detail & Related papers  (2024-09-17T14:36:58Z)
• Monocular Gaussian SLAM with Language Extended Loop Closure [32.0451093146944]
  Existing methods have achieved encouraging results on RGB-D SLAM, but studies of the monocular case are still scarce. We present MG-SLAM, a monocular Gaussian SLAM with a language-extended loop closure module capable of performing drift-corrected tracking and high-fidelity reconstruction. Our system shows promising results on multiple challenging datasets in both tracking and mapping and even surpasses some existing RGB-D methods.
  arXiv  Detail & Related papers  (2024-05-22T15:33:23Z)
• Loopy-SLAM: Dense Neural SLAM with Loop Closures [53.11936461015725]
  We introduce Loopy-SLAM that globally optimize poses and the dense 3D model. We use frame-to-model tracking using a data-driven point-based submap generation method and trigger loop closures online by performing global place recognition. Evaluation on the synthetic Replica and real-world TUM-RGBD and ScanNet datasets demonstrate competitive or superior performance in tracking, mapping, and rendering accuracy when compared to existing dense neural RGBD SLAM methods.
  arXiv  Detail & Related papers  (2024-02-14T18:18:32Z)
• DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking and Loop-Closing [13.50980509878613]
  Experimental evaluations on publicly available datasets demonstrate that DK-SLAM outperforms leading traditional and learning based SLAM systems. Our system employs a Model-Agnostic Meta-Learning (MAML) strategy to optimize the training of keypoint extraction networks. To mitigate cumulative positioning errors, DK-SLAM incorporates a novel online learning module that utilizes binary features for loop closure detection.
  arXiv  Detail & Related papers  (2024-01-17T12:08:30Z)
• SemanticTopoLoop: Semantic Loop Closure With 3D Topological Graph Based on Quadric-Level Object Map [0.8158530638728501]
  Loop closure is one of the crucial components in SLAM. Traditional appearance-based methods, such as bag-of-words models, are often limited by local 2D features and the volume of training data.
  arXiv  Detail & Related papers  (2023-11-06T02:30:30Z)
• Exploiting Low-confidence Pseudo-labels for Source-free Object Detection [54.98300313452037]
  Source-free object detection (SFOD) aims to adapt a source-trained detector to an unlabeled target domain without access to the labeled source data. Current SFOD methods utilize a threshold-based pseudo-label approach in the adaptation phase. We propose a new approach to take full advantage of pseudo-labels by introducing high and low confidence thresholds.
  arXiv  Detail & Related papers  (2023-10-19T12:59:55Z)
• Volumetric Semantically Consistent 3D Panoptic Mapping [77.13446499924977]
  We introduce an online 2D-to-3D semantic instance mapping algorithm aimed at generating semantic 3D maps suitable for autonomous agents in unstructured environments. It introduces novel ways of integrating semantic prediction confidence during mapping, producing semantic and instance-consistent 3D regions. The proposed method achieves accuracy superior to the state of the art on public large-scale datasets, improving on a number of widely used metrics.
  arXiv  Detail & Related papers  (2023-09-26T08:03:10Z)
• CONVERT:Contrastive Graph Clustering with Reliable Augmentation [110.46658439733106]
  We propose a novel CONtrastiVe Graph ClustEring network with Reliable AugmenTation (CONVERT) In our method, the data augmentations are processed by the proposed reversible perturb-recover network. To further guarantee the reliability of semantics, a novel semantic loss is presented to constrain the network.
  arXiv  Detail & Related papers  (2023-08-17T13:07:09Z)
• Rapid Adaptation in Online Continual Learning: Are We Evaluating It Right? [135.71855998537347]
  We revisit the common practice of evaluating adaptation of Online Continual Learning (OCL) algorithms through the metric of online accuracy. We show that this metric is unreliable, as even vacuous blind classifiers can achieve unrealistically high online accuracy. Existing OCL algorithms can also achieve high online accuracy, but perform poorly in retaining useful information.
  arXiv  Detail & Related papers  (2023-05-16T08:29:33Z)
• An Online Semantic Mapping System for Extending and Enhancing Visual SLAM [2.538209532048867]
  We present a real-time semantic mapping approach for mobile vision systems with a 2D to 3D object detection pipeline and rapid data association for generated landmarks. Our system reaches real-time capabilities with an average iteration duration of 65ms and is able to improve the pose estimation of a state-of-the-art SLAM by up to 68% on a public dataset.
  arXiv  Detail & Related papers  (2022-03-08T09:14:37Z)
• 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)

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.