Fast Sequence-Matching Enhanced Viewpoint-Invariant 3-D Place Recognition

Yin, Peng; Wang, Fuying; Egorov, Anton; Hou, Jiafan; Jia, Zhenzhong; Han, Jianda

doi:10.1109/tie.2021.3057025

Cited by 13 publications

(4 citation statements)

References 23 publications

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“…More recently, Yin et al [35] propose FusionVLAD to generate multi-view representations with dense submaps from sequential LiDAR scans, and encode both the top-down and spherical views of LiDAR scans. They later also propose SeqSphereVLAD [36], [37], which locates the best match using a particle filter-based method in the global searching thus improving the place recognition robustness. These methods have achieved comparable performance by combining singlescan/submap-based place recognition methods with sequence matching.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, we propose an end-to-end sequence-enhanced place recognition method. Different to the existing sequence-enhanced methods [12], [8], [36], [37], [9], we fuse the spatial and temporal information using yaw-rotation-invariant transformer networks, and directly generate one single global descriptor for each LiDAR sequence in an end-to-end fashion for fast LiDAR sequencebased place recognition.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

SeqOT: A Spatial-Temporal Transformer Network for Place Recognition Using Sequential LiDAR Data

Ma¹,

Chen²,

Xu³

et al. 2022

Preprint

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Place recognition is an important component for autonomous vehicles to achieve loop closing or global localization. In this paper, we tackle the problem of place recognition based on sequential 3D LiDAR scans obtained by an onboard LiDAR sensor. We propose a transformer-based network named SeqOT to exploit the temporal and spatial information provided by sequential range images generated from the LiDAR data. It uses multi-scale transformers to generate a global descriptor for each sequence of LiDAR range images in an end-to-end fashion. During online operation, our SeqOT finds similar places by matching such descriptors between the current query sequence and those stored in the map. We evaluate our approach on four datasets collected with different types of LiDAR sensors in different environments. The experimental results show that our method outperforms the state-of-the-art LiDAR-based place recognition methods and generalizes well across different environments. Furthermore, our method operates online faster than the frame rate of the sensor. The implementation of our method is released as open source at: https://github.com/BIT-MJY/SeqOT.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

SeqOT: A Spatial-Temporal Transformer Network for Place Recognition Using Sequential LiDAR Data

Ma¹,

Chen²,

Xu³

et al. 2022

Preprint

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“…However, offline maps typically entail higher memory consumption and necessitate timely updates. Some researchers incorporate transformers (Ma et al., 2022), semantics (Li, Kong, Zhao, Li, et al., 2021), and sequence data (Yin et al., 2022) into loop detection, but these methods demand extensive computational resources and warrant improvements in generalization capabilities. In summary, LCD remains an unsolved challenge in LiDAR SLAM, and achieving faster efficiency and higher accuracy recognition represents a complex and promising direction for future research.…”

Section: Introductionmentioning

confidence: 99%

3D LiDAR SLAM: A survey

Zhang,

Shi,

2024

The Photogrammetric Record

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Simultaneous localization and mapping (SLAM) is a very challenging yet fundamental problem in the field of robotics and photogrammetry, and it is also a prerequisite for intelligent perception of unmanned systems. In recent years, 3D LiDAR SLAM technology has made remarkable progress. However, to the best of our knowledge, almost all existing surveys focus on visual SLAM methods. To bridge the gap, this paper provides a comprehensive review that summarizes the scientific connotation, key difficulties, research status, and future trends of 3D LiDAR SLAM, aiming to give readers a better understanding of LiDAR SLAM technology, thereby inspiring future research. Specifically, it summarizes the contents and characteristics of the main steps of LiDAR SLAM, introduces the key difficulties it faces, and gives the relationship with existing reviews; it provides an overview of current research hotspots, including LiDAR‐only methods and multi‐sensor fusion methods, and gives milestone algorithms and open‐source tools in each category; it summarizes common datasets, evaluation metrics and representative commercial SLAM solutions, and provides the evaluation results of mainstream methods on public datasets; it looks forward to the development trend of LiDAR SLAM, and considers the preliminary ideas of multi‐modal SLAM, event SLAM, and quantum SLAM.

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“…With the recent monumental innovations in sensor technology, a wide variety of DL-based 3D object [25][26][27][28] and place recognition approaches [29][30][31] have been developed for different types of sensors. LiDAR and camera are two frequently used and increasingly popular sensors [32] that have been employed for object and place recognition in robotic systems.…”

Section: Introductionmentioning

confidence: 99%

3D Recognition Based on Sensor Modalities for Robotic Systems: A Survey

Manzoor

Joo

Kim

et al. 2021

Sensors

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3D visual recognition is a prerequisite for most autonomous robotic systems operating in the real world. It empowers robots to perform a variety of tasks, such as tracking, understanding the environment, and human–robot interaction. Autonomous robots equipped with 3D recognition capability can better perform their social roles through supportive task assistance in professional jobs and effective domestic services. For active assistance, social robots must recognize their surroundings, including objects and places to perform the task more efficiently. This article first highlights the value-centric role of social robots in society by presenting recently developed robots and describes their main features. Instigated by the recognition capability of social robots, we present the analysis of data representation methods based on sensor modalities for 3D object and place recognition using deep learning models. In this direction, we delineate the research gaps that need to be addressed, summarize 3D recognition datasets, and present performance comparisons. Finally, a discussion of future research directions concludes the article. This survey is intended to show how recent developments in 3D visual recognition based on sensor modalities using deep-learning-based approaches can lay the groundwork to inspire further research and serves as a guide to those who are interested in vision-based robotics applications.

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Fast Sequence-Matching Enhanced Viewpoint-Invariant 3-D Place Recognition

Cited by 13 publications

References 23 publications

SeqOT: A Spatial-Temporal Transformer Network for Place Recognition Using Sequential LiDAR Data

SeqOT: A Spatial-Temporal Transformer Network for Place Recognition Using Sequential LiDAR Data

3D LiDAR SLAM: A survey

3D Recognition Based on Sensor Modalities for Robotic Systems: A Survey

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