LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition

Vidanapathirana, Kavisha; Ramezani, Milad; Moghadam, Peyman; Sridharan, Sridha; Fookes, Clinton

doi:10.1109/icra46639.2022.9811753

Cited by 39 publications

(15 citation statements)

References 25 publications

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“…SegMap proposed by Dubé et al [27], [28] first extracts the segments from the point clouds as the input of the devised convolution neural network. LoGG3D-Net by Vidanapathirana et al [10] exploits sparse convolution directly on raw point clouds, and considers both local consistency loss and scene-level loss. In contrast, MinkLoc3D by Komorowski [13] combines the sparse voxelized representation and convolutions to tackle the unordered set problem.…”

Section: Related Workmentioning

confidence: 99%

“…LiDARbased place recognition methods [6], [7], [8] can be applied to large-scale outdoor environments due to their robustness to illumination and weather changes. Different representation forms of LiDAR data have been exploited as input for LPR methods, such as 3D point clouds [9], [10], [11], voxels [12], [13], [14], normal distributions transform cells [15], [16], bird's eye views (BEVs) [17], [18], [19], and range image views (RIVs) [1], [3], [20]. However, few methods fuse them together to exploit the advances of different views.…”

Section: Introductionmentioning

confidence: 99%

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CVTNet: A Cross-View Transformer Network for Place Recognition Using LiDAR Data

Ma¹,

Guan²,

Xu³

et al. 2023

Preprint

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LiDAR-based place recognition (LPR) is one of the most crucial components of autonomous vehicles to identify previously visited places in GPS-denied environments. Most existing LPR methods use mundane representations of the input point cloud without considering different views, which may not fully exploit the information from LiDAR sensors. In this paper, we propose a cross-view transformer-based network, dubbed CVTNet, to fuse the range image views (RIVs) and bird's eye views (BEVs) generated from the LiDAR data. It extracts correlations within the views themselves using intra-transformers and between the two different views using inter-transformers.Based on that, our proposed CVTNet generates a yaw-angleinvariant global descriptor for each laser scan end-to-end online and retrieves previously seen places by descriptor matching between the current query scan and the pre-built database. We evaluate our approach on three datasets collected with different sensor setups and environmental conditions. The experimental results show that our method outperforms the state-of-the-art LPR methods with strong robustness to viewpoint changes and long-time spans. Furthermore, our approach has a good realtime performance that can run faster than the typical LiDAR frame rate. The implementation of our method is released as open source at: https://github.com/BIT-MJY/CVTNet.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CVTNet: A Cross-View Transformer Network for Place Recognition Using LiDAR Data

Ma¹,

Guan²,

Xu³

et al. 2023

Preprint

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“…However, such high-level semantic information is not always available in different environments. To exploit point cloud 3D spatial information for place recognition, Minkloc3D by Komorowski [28] and LoGG3D-Net by Vidanapathirana et al [29] use sparse convolution for effectively extracting point-wise features. More recently, attention mechanism [30] has been introduced to generate more discriminative descriptors for LiDAR-based place recognition.…”

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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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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“…Place recognition is of significant importance for the loop closing detection, thereby many researches have been conducted. However, there are still difficulties in performing place Recognition for 3D LiDAR point clouds, because 3D LiDAR point clouds are sparse compared to 2D images and have complex distributions of 3D geometric structures [8]. In this case, information that can distinguish geometric structures may not be incorporated into the features, which can cause the failure to detect course-level previously visited places.…”

Section: Introductionmentioning

confidence: 99%

“…With fewer outliers, the accuracy of 6-DOF calculations during RANSAC or SVD will increase, enabling robust point cloud registration [15]. In addition, the better "local consistency" of features can also lead to improvement in global place recognition [8]. To determine the necessity of each feature point for learning, in this paper two techniques are used.…”

Section: Introductionmentioning

confidence: 99%

HI-FFT: Heterogeneous Parallel In-Place Algorithm for Large-Scale 2D-FFT

Kang

Lee

Kim³

2021

IEEE Access

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Fast Fourier Transform (FFT) is a fundamental operation for 2D data in various applications. To accelerate large-scale 2D-FFT computation, we propose a Heterogeneous parallel In-place 2D-FFT algorithm, HI-FFT. Our novel work decomposition method makes it possible to run our parallel algorithm on the original data (i.e., in-place), unlike prior parallel algorithms that require additional memory space (i.e., out-of-place) to guarantee independence among sub-tasks. Our work decomposition method also removes the duplicated operations on the out-of-place approaches. Using our decomposition method, we introduced an in-place heterogeneous parallel algorithm that utilizes both multi-core CPU and GPU simultaneously. To maximize the utilization efficiency of the computing resources, we also propose a priority-based dynamic scheduling method. We compared the performance of seven different 2D-FFT algorithms, including ours, for large-scale 2D-FFT problems whose sizes varied from 20K 2 to 120K 2 . As a result, we found that our method achieved up to 2.92 and 4.42 times higher performance than the conventional homogeneous parallel algorithms based on the state-of-the-art CPU and GPU libraries, respectively. Also, our method showed up to 2.27 times higher performance than the prior heterogeneous algorithms while requiring two times less memory space.To check the benefit of our HI-FFT on an actual application, we applied it to a CGH (Computer Generated Holography) process. We found that it successfully reduces the hologram generation time. These results demonstrate the advantage of our approach for large-scale 2D-FFT computation.

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LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition

Cited by 39 publications

References 25 publications

CVTNet: A Cross-View Transformer Network for Place Recognition Using LiDAR Data

CVTNet: A Cross-View Transformer Network for Place Recognition Using LiDAR Data

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

HI-FFT: Heterogeneous Parallel In-Place Algorithm for Large-Scale 2D-FFT

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