Role of Deep Learning in Loop Closure Detection for Visual and Lidar SLAM: A Survey

Arshad, Saba; Kim, Gon–Woo

doi:10.3390/s21041243

Cited by 79 publications

(38 citation statements)

References 121 publications

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“…CNN has achieved good results in replacing some modules of the traditional VSLAM algorithm, such as depth estimation and loop closure detection. Its stability is still not as good as the traditional VSLAM algorithm [172]. In contrast, the semantic information extraction of the CNN system has brought better effects.…”

Section: Global Map and Semantic Label Fusionmentioning

confidence: 92%

An Overview on Visual SLAM: From Tradition to Semantic

et al. 2022

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Visual SLAM (VSLAM) has been developing rapidly due to its advantages of low-cost sensors, the easy fusion of other sensors, and richer environmental information. Traditional visionbased SLAM research has made many achievements, but it may fail to achieve wished results in challenging environments. Deep learning has promoted the development of computer vision, and the combination of deep learning and SLAM has attracted more and more attention. Semantic information, as high-level environmental information, can enable robots to better understand the surrounding environment. This paper introduces the development of VSLAM technology from two aspects: traditional VSLAM and semantic VSLAM combined with deep learning. For traditional VSLAM, we summarize the advantages and disadvantages of indirect and direct methods in detail and give some classical VSLAM open-source algorithms. In addition, we focus on the development of semantic VSLAM based on deep learning. Starting with typical neural networks CNN and RNN, we summarize the improvement of neural networks for the VSLAM system in detail. Later, we focus on the help of target detection and semantic segmentation for VSLAM semantic information introduction. We believe that the development of the future intelligent era cannot be without the help of semantic technology. Introducing deep learning into the VSLAM system to provide semantic information can help robots better perceive the surrounding environment and provide people with higher-level help.

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Section: Global Map and Semantic Label Fusionmentioning

confidence: 92%

An Overview on Visual SLAM: From Tradition to Semantic

et al. 2022

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“…point clouds. Readers are referred to [1] for a more thorough review on general loop closure methods. Existing research on 3D-based loop detection can be categorised into three groups [22]: feature-based [7], [8], [11], [23]- [25], segmentation-based [26], [27], and learningbased methods [9], [10], [28], [29].…”

Section: Related Workmentioning

confidence: 99%

“…Loop closure aims to recognise already visited places in order to mitigate tracking drifts in simultaneous localisation and mapping (SLAM) [1]. Correctly handling loop closures can improve localisation accuracy of an autonomous robot [2].…”

Section: Introductionmentioning

confidence: 99%

Loop closure detection using local 3D deep descriptors

Zhou¹,

Wang²,

Poiesi³

et al. 2021

Preprint

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We present a simple yet effective method to address loop closure detection in simultaneous localisation and mapping using local 3D deep descriptors (L3Ds). L3Ds are emerging compact representations of patches extracted from point clouds that are learned from data using a deep learning algorithm. We propose a novel overlap measure for loop detection by computing the metric error between points that correspond to mutually-nearest-neighbour descriptors after registering the loop candidate point cloud by its estimated relative pose. This novel approach enables us to accurately detect loops and estimate six degrees-of-freedom poses in the case of small overlaps. We compare our L3D-based loop closure approach with recent approaches on LiDAR data and achieve state-of-theart loop closure detection accuracy. Additionally, we embed our loop closure approach in RESLAM, a recent edge-based SLAM system, and perform the evaluation on real-world RGBD-TUM and synthetic ICL datasets. Our approach enables RESLAM to achieve a better localisation accuracy compared to its original loop closure strategy.

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“…Thanks to the rapid development of deep learning [1,2] and various sensors, the techniques of the real-world scene sensing, analysis, and management are improved constantly, which potentially boosts the development of autonomous driving [3], robotic [4], remote sensing [5], medical science [6,7], the internet of things [8], etc. Therefore, the task of segmentation [9,10] and detection [11,12], the basic tasks of scene understanding, have achieved great improvements recently.…”

Section: Introductionmentioning

confidence: 99%

The Fusion Strategy of 2D and 3D Information Based on Deep Learning: A Review

et al. 2021

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Recently, researchers have realized a number of achievements involving deep-learning-based neural networks for the tasks of segmentation and detection based on 2D images, 3D point clouds, etc. Using 2D and 3D information fusion for the advantages of compensation and accuracy improvement has become a hot research topic. However, there are no critical reviews focusing on the fusion strategies of 2D and 3D information integration based on various data for segmentation and detection, which are the basic tasks of computer vision. To boost the development of this research domain, the existing representative fusion strategies are collected, introduced, categorized, and summarized in this paper. In addition, the general structures of different kinds of fusion strategies were firstly abstracted and categorized, which may inspire researchers. Moreover, according to the methods included in this paper, the 2D information and 3D information of different methods come from various kinds of data. Furthermore, suitable datasets are introduced and comparatively summarized to support the relative research. Last but not least, we put forward some open challenges and promising directions for future research.

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Role of Deep Learning in Loop Closure Detection for Visual and Lidar SLAM: A Survey

Cited by 79 publications

References 121 publications

An Overview on Visual SLAM: From Tradition to Semantic

An Overview on Visual SLAM: From Tradition to Semantic

Loop closure detection using local 3D deep descriptors

The Fusion Strategy of 2D and 3D Information Based on Deep Learning: A Review

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