A Flexible and Efficient Loop Closure Detection Based on Motion Knowledge

Liu, Bingxi; Tang, Fulin; Fu, Yujie; Yang, Yanqun; Wu, Yihong

doi:10.1109/icra48506.2021.9561126

Cited by 3 publications

(7 citation statements)

References 33 publications

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“…Place recognition can also be formulated as a coarse to fine image matching problem. An initial set of reference image candidates is obtained based on nearest neighbor distances of image‐wise global descriptors (Camara et al, 2020; Liu et al, 2021; Xin et al, 2017). Then, local features are used to obtain a more accurate estimation based on spatial matching (Camara et al, 2020; Xin et al, 2017) or geometrical verification (Liu et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…An initial set of reference image candidates is obtained based on nearest neighbor distances of image‐wise global descriptors (Camara et al, 2020; Liu et al, 2021; Xin et al, 2017). Then, local features are used to obtain a more accurate estimation based on spatial matching (Camara et al, 2020; Xin et al, 2017) or geometrical verification (Liu et al, 2021). Xin et al (2017) extract both global and local features using a convolutional layer ( conv3 ) of the AlexNet network.…”

Section: Discussionmentioning

confidence: 99%

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A systematic literature review on long‐term localization and mapping for mobile robots

Sousa

Sobreira

Moreira

2023

Journal of Field Robotics

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Long‐term operation of robots creates new challenges to Simultaneous Localization and Mapping (SLAM) algorithms. Long‐term SLAM algorithms should adapt to recent changes while preserving older states, when dealing with appearance variations (lighting, daytime, weather, or seasonal) or environment reconfiguration. When also operating robots for long periods and trajectory lengths, the map should readjust to environment changes but not grow indefinitely. The map size should depend only on updating the map with new information of interest, not on the operation time or trajectory length. Although several studies in the literature review SLAM algorithms, none of the studies focus on the challenges associated to lifelong SLAM. Thus, this paper presents a systematic literature review on long‐term localization and mapping following the Preferred Reporting Items for Systematic reviews and Meta‐Analysis guidelines. The review analyzes 142 works covering appearance invariance, modeling the environment dynamics, map size management, multisession, and computational topics such as parallel computing and timing efficiency. The analysis also focus on the experimental data and evaluation metrics commonly used to assess long‐term autonomy. Moreover, an overview over the bibliographic data of the 142 records provides analysis in terms of keywords and authorship co‐occurrence to identify the terms more used in long‐term SLAM and research networks between authors, respectively. Future studies can update this paper thanks to the systematic methodology presented in the review and the public GitHub repository with all the documentation and scripts used during the review process.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A systematic literature review on long‐term localization and mapping for mobile robots

Sousa

Sobreira

Moreira

2023

Journal of Field Robotics

View full text Add to dashboard Cite

show abstract

“…Place recognition can also be formulated as a coarse to fine image matching problem. An initial set of reference image candidates is obtained based on nearest neighbor distances of imagewise global descriptors (Camara et al 2020;B. Liu et al 2021;Xin et al 2017), while local features are used for obtaining a more accurate estimation based on spatial matching (Camara et al 2020;Xin et al 2017) Given that feature maps can extract different types of features depending on the deepness of the respective layers, J.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

“…Lastly, a trend found in the included works to improve the discriminative power of CNN features is the use of triplets (B. Liu et al 2021;Martini et al 2020;Piasco et al 2021;Li Sun et al 2021;P. Yin, J. Xu, et al 2021) in training.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

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A Systematic Literature Review on Long-Term Localization and Mapping for Mobile Robots

Sousa

Sobreira

Moreira

2022

Preprint

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Long-term operation of autonomous robots creates new challenges to the Simultaneous Localization and Mapping (SLAM). Varying conditions of the vehicle’s surroundings, such as appearance variations (lighting, daytime, weather, or seasonal) or reconfigurations of the environment, are a challenge for SLAM algorithms to adapt to new changes while preserving old states. When also operating for long periods and trajectory lengths, the map should readjust to environment changes but not grow indefinitely, where the map size should be dependent only on the explored environment area. Long-term SLAM intends to overcome the challenges associated with lifelong autonomy and improve the robustness of autonomous systems. Although several studies review SLAM algorithms, none of them focus on lifelong autonomy. Thus, this paper presents a systematic literature review on long-term localization and mapping following the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines. The review analyzes 142 works covering appearance invariance, modeling the environment dynamics, map size management, multi-session, and computational issues including parallel computing and timming efficiency. The analysis also focus on the experimental data and evaluation metrics commonly used to assess long-term autonomy. Moreover, an overview over the bibliographic data of the 142 records provides analysis in terms of keywords and authorship co-occurrence to identify the terms more used in long-term SLAM and research networks between authors, respectively. Future studies can update this paper thanks to the systematic methodology presented in the review and the public GitHub repository with all the documentation and scripts used during the review process.

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Automatic vocabulary and graph verification for accurate loop closure detection

Yue

Miao

Chen

et al. 2022

Journal of Field Robotics

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Localizing previously visited places during long‐term localization and mapping, that is, loop closure detection (LCD), is a crucial technique to correct accumulated inconsistencies. In common bag‐of‐words (BoW) model, a visual vocabulary is built to associate features for detecting loops. Currently, methods that build vocabularies off‐line determine scales of the vocabulary by trial‐and‐error, which results in unreasonable feature association. Moreover, the detection precision of the algorithm declines due to perceptual aliasing given that the BoW‐based method ignores the positions of visual features. To build the optimal vocabulary automatically and eliminate human heuristics, we propose a natural convergence criterion based on the comparison between the radii of nodes and the drifts of feature descriptors in vocabulary construction. Furthermore, a novel topological graph verification method is proposed for validating loop candidates, which can effectively distinguish visual ambiguities by involving geometrical position of features and thus improve the precision of LCD. Experiments on various public datasets verify the effectiveness of our proposed approach.

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A Flexible and Efficient Loop Closure Detection Based on Motion Knowledge

Cited by 3 publications

References 33 publications

A systematic literature review on long‐term localization and mapping for mobile robots

A systematic literature review on long‐term localization and mapping for mobile robots

A Systematic Literature Review on Long-Term Localization and Mapping for Mobile Robots

Automatic vocabulary and graph verification for accurate loop closure detection

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