Appearance-Based Loop Closure Detection via Locality-Driven Accurate Motion Field Learning

Zhang, Kaining; Jiang, X. Y.; Ma, Jiayi

doi:10.1109/tits.2021.3086822

Cited by 15 publications

(4 citation statements)

References 60 publications

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“…Deep features can be combined with handcrafted features and preprocessing techniques to facilitate learning and further enhance their discriminative properties. Zhang et al (2022) use the Key.Net (Laguna et al, 2019) network for keypoint generation. This network combines handcrafted and learned filters to detect keypoints at different scale levels, reducing the number of learnable parameters.…”

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%

“…Deep features can be combined with handcrafted features and preprocessing techniques to facilitate learning and further enhance their discriminative properties Zhang et al (2022). use the Key.Net(Laguna et al, 2019) network for keypoint generation.…”

mentioning

confidence: 99%

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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“…As such, a robust loop closure detection mechanism is vital for accurate localization and mapping results. Appearance-based loop closure detection algorithms [1][2][3][4][5][6] have gained increasing attention due to the rapid development of visual SLAM algorithms that use cameras as external perception sensors.…”

Section: Introductionmentioning

confidence: 99%

Incremental Bag of Words with Gradient Orientation Histogram for Appearance-Based Loop Closure Detection

Zhu³

2023

Applied Sciences

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This paper proposes a novel approach for appearance-based loop closure detection using incremental Bag of Words (BoW) with gradient orientation histograms. The presented approach involves dividing and clustering image blocks into local region features and representing them using gradient orientation histograms. To improve the efficiency of the loop closure detection process, the vocabulary Clustering Feature (CF) tree is generated and updated in real time using the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm, which is combined with an inverted index for the efficient selection of candidates and calculation of similarity. Moreover, temporally close and highly similar images are grouped to generate islands, which enhances the accuracy and efficiency of the loop closure detection process. The proposed approach is evaluated on publicly available datasets, and the results demonstrate high recall and precision.

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“…However, their work uses grid-based motion statistics with ORB local features instead of CNN features.Deep features can be combined with handcrafted features and preprocessing techniques to facilitate learning and further enhance their discriminative properties. K Zhang et al 2022. uses the Key.Net network for keypoint generation, given that combines handcrafted and learned filters to detect keypoints at different scale levels, helping reduce the number of learnable parameters.…”

mentioning

confidence: 99%

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.

show abstract

Appearance-Based Loop Closure Detection via Locality-Driven Accurate Motion Field Learning

Cited by 15 publications

References 60 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

Incremental Bag of Words with Gradient Orientation Histogram for Appearance-Based Loop Closure Detection

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

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