DAGC: Employing Dual Attention and Graph Convolution for Point Cloud based Place Recognition

Sun, Qi; Liu, Hongyan; He, Jun; Fan, Zhaoxin; Du, Xiaoyong

doi:10.1145/3372278.3390693

Cited by 41 publications

(30 citation statements)

References 35 publications

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“…Unfortunately, the method suffers from PointNet weakness to capture high-level features. Therefore, many solutions like [12], [13], [15] focuse on the data representation problem leaving the NetVLAD part intact. PCAN [12] improves PointNet by estimating the significance of each point.…”

Section: B 3d Lidar Place Recognitionmentioning

confidence: 99%

“…PCAN [12] improves PointNet by estimating the significance of each point. DAGC [15] uses graph CNN to combine information at multiple scales. LPD-NET [13] computes hand-crafted features, which are later processed using a pipeline similar to PointNet architecture.…”

Section: B 3d Lidar Place Recognitionmentioning

confidence: 99%

“…More recently, we also see a rise in sparse convolution-based approaches [8], [9] and attentionbased modules [10] that might be combined together [11] as well. Many of these methods are trained, evaluated, and compared on the Oxford RobotCar dataset [6], [8], [12], [13], [14], [15], [16], [17], which was gathered by concatenating multiple 2D LiDAR scans covering a 20 m distance and subsampling to 4096 points [6]. In contrast, a single scan from a modern 3D LiDAR covers a much larger area (even up to 160-200 meters in diameter) and has a greater number of points (up to 260k points for Ouster OS1-128), as presented in Fig 1.…”

Section: Introductionmentioning

confidence: 99%

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MinkLoc3D-SI: 3D LiDAR Place Recognition With Sparse Convolutions, Spherical Coordinates, and Intensity

Kamil

Banaszczyk

Nowicki

et al. 2022

IEEE Robot. Autom. Lett.

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The 3D LiDAR place recognition aims to estimate a coarse localization in a previously seen environment based on a single scan from a rotating 3D LiDAR sensor. The existing solutions to this problem include hand-crafted point cloud descriptors (e.g., ScanContext, M2DP, LiDAR IRIS) and deep learning-based solutions (e.g., PointNetVLAD, PCAN, LPD-Net, DAGC, MinkLoc3D), which are often only evaluated on accumulated 2D scans from the Oxford RobotCar dataset. We introduce MinkLoc3D-SI, a sparse convolution-based solution that utilizes spherical coordinates of 3D points and processes the intensity of 3D LiDAR measurements, improving the performance when a single 3D LiDAR scan is used. Our method integrates the improvements typical for hand-crafted descriptors (like ScanContext) with the most efficient 3D sparse convolutions (MinkLoc3D). Our experiments show improved results on single scans from 3D LiDARs (USyd Campus dataset) and great generalization ability (KITTI dataset). Using intensity information on accumulated 2D scans (RobotCar Intensity dataset) improves the performance, even though spherical representation doesn't produce a noticeable improvement. As a result, MinkLoc3D-SI is suited for single scans obtained from a 3D LiDAR, making it applicable in autonomous vehicles.

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Section: B 3d Lidar Place Recognitionmentioning

confidence: 99%

Section: B 3d Lidar Place Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MinkLoc3D-SI: 3D LiDAR Place Recognition With Sparse Convolutions, Spherical Coordinates, and Intensity

Kamil

Banaszczyk

Nowicki

et al. 2022

IEEE Robot. Autom. Lett.

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“…There are two main challenges to perform visual place recognition based on the differences of scenes: one is the appearance change caused by illumination condition and seasonal changes, and the other is the viewpoint change caused by revisiting one place from different viewpoints [1]. In the VPR literature, various feature extraction methods have been developed for visual place recognition, including deep convolutional feature-based methods [6][7][8][9][10][11][12][13], handicraft feature-based methods [2,18], semantic information-based methods [19][20][21][22][23][24][25], sequence-based methods [26,27], and graph-based methods [19,20,[28][29][30][31][32]. Overall, most of these studies focus on the image processing module of the visual place recognition system, which aims to extract and describe features that are robust in the different challenge conditions as mentioned above.…”

Section: Visual Place Recognitionmentioning

confidence: 99%

GSAP: A Global Structure Attention Pooling Method for Graph-Based Visual Place Recognition

Yang

Liu

et al. 2021

Remote Sensing

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The Visual Place Recognition problem aims to use an image to recognize the location that has been visited before. In most of the scenes revisited, the appearance and view are drastically different. Most previous works focus on the 2-D image-based deep learning method. However, the convolutional features are not robust enough to the challenging scenes mentioned above. In this paper, in order to take advantage of the information that helps the Visual Place Recognition task in these challenging scenes, we propose a new graph construction approach to extract the useful information from an RGB image and a depth image and fuse them in graph data. Then, we deal with the Visual Place Recognition problem as a graph classification problem. We propose a new Global Pooling method—Global Structure Attention Pooling (GSAP), which improves the classification accuracy by improving the expression ability of the Global Pooling component. The experiments show that our GSAP method improves the accuracy of graph classification by approximately 2–5%, the graph construction method improves the accuracy of graph classification by approximately 4–6%, and that the whole Visual Place Recognition model is robust to appearance change and view change.

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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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DAGC: Employing Dual Attention and Graph Convolution for Point Cloud based Place Recognition

Cited by 41 publications

References 35 publications

MinkLoc3D-SI: 3D LiDAR Place Recognition With Sparse Convolutions, Spherical Coordinates, and Intensity

MinkLoc3D-SI: 3D LiDAR Place Recognition With Sparse Convolutions, Spherical Coordinates, and Intensity

GSAP: A Global Structure Attention Pooling Method for Graph-Based Visual Place Recognition

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

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