374 research outputs found
SeqOT: A Spatial-Temporal Transformer Network for Place Recognition Using Sequential LiDAR Data
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.Comment: Submitted to IEEE Transactions on Industrial Electronic
CVTNet: A Cross-View Transformer Network for Place Recognition Using LiDAR Data
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-angle-invariant 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
real-time 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.Comment: accepted by IEEE Transactions on Industrial Informatics 202
LCPR: A Multi-Scale Attention-Based LiDAR-Camera Fusion Network for Place Recognition
Place recognition is one of the most crucial modules for autonomous vehicles
to identify places that were previously visited in GPS-invalid environments.
Sensor fusion is considered an effective method to overcome the weaknesses of
individual sensors. In recent years, multimodal place recognition fusing
information from multiple sensors has gathered increasing attention. However,
most existing multimodal place recognition methods only use limited
field-of-view camera images, which leads to an imbalance between features from
different modalities and limits the effectiveness of sensor fusion. In this
paper, we present a novel neural network named LCPR for robust multimodal place
recognition, which fuses LiDAR point clouds with multi-view RGB images to
generate discriminative and yaw-rotation invariant representations of the
environment. A multi-scale attention-based fusion module is proposed to fully
exploit the panoramic views from different modalities of the environment and
their correlations. We evaluate our method on the nuScenes dataset, and the
experimental results show that our method can effectively utilize multi-view
camera and LiDAR data to improve the place recognition performance while
maintaining strong robustness to viewpoint changes. Our open-source code and
pre-trained models are available at https://github.com/ZhouZijie77/LCPR .Comment: Accepted by IEEE Robotics and Automation Letters (RAL) 202
A novel cooperative platform design for coupled USV-UAV systems
International audienceThis paper presents a novel cooperative USV-UAV platform to form a powerful combination, which offers foundations for collaborative task executed by the coupled USV-UAV systems. Adjustable buoys and unique carrier deck for the USV are designed to guarantee landing safety and transportation of UAV. The deck of USV is equipped with a series of sensors, and a multi-ultrasonic joint dynamic positioning algorithm is introduced for resolving the positioning problem of the coupled USV-UAV systems. To fulfill effective guidance for the landing operation of UAV, we design a hierarchical landing guide point generation algorithm to obtain a sequence of guide points. By employing the above sequential guide points, high quality paths are planned for the UAV. Cooperative dynamic positioning process of the USV-UAV systems is elucidated, and then UAV can achieve landing on the deck of USV steadily. Our cooperative USV-UAV platform is validated by simulation and water experiments. Index Terms-USV-UAV platform. Multi-ultrasonic joint dynamic positioning algorithm. Hierarchical landing guide point generation algorithm. Cooperative positioning
Retinex-guided Channel-grouping based Patch Swap for Arbitrary Style Transfer
The basic principle of the patch-matching based style transfer is to
substitute the patches of the content image feature maps by the closest patches
from the style image feature maps. Since the finite features harvested from one
single aesthetic style image are inadequate to represent the rich textures of
the content natural image, existing techniques treat the full-channel style
feature patches as simple signal tensors and create new style feature patches
via signal-level fusion, which ignore the implicit diversities existed in style
features and thus fail for generating better stylised results. In this paper,
we propose a Retinex theory guided, channel-grouping based patch swap technique
to solve the above challenges. Channel-grouping strategy groups the style
feature maps into surface and texture channels, which prevents the
winner-takes-all problem. Retinex theory based decomposition controls a more
stable channel code rate generation. In addition, we provide complementary
fusion and multi-scale generation strategy to prevent unexpected black area and
over-stylised results respectively. Experimental results demonstrate that the
proposed method outperforms the existing techniques in providing more
style-consistent textures while keeping the content fidelity
Loureirin B attenuates amiodarone-induced pulmonary fibrosis by suppression of TGFβ1/Smad2/3 pathway
Purpose: To investigate the therapeutic effect of loureirin B (LB) on amiodarone (AD)-induced pulmonary fibrosis (PF).Methods: Forty-eight male C57BL/6 mice, 8–10 weeks of age, were divided into four groups (n=12). Oral administration of amiodarone hydrochloride (AD) was performed for 4 weeks to induce pulmonary fibrosis. The degree of fibrosis was assessed by Masson staining, while collagen I and α-smooth muscle actin (α-SMA) levels were evaluated by Western blot analysis. ELISA was used to measure the levels of cytokines TNF-α, IL-1β, and IL-6 in bronchoalveolar lavage fluid (BALF) and lung tissue. Levels of p- Smad2, Smad2, p-Smad3 and Smad3 were determined by western blotting.Results: AD treatment increased the collagen levels and expression levels of collagen I and α-smooth muscle actin (α-SMA) in lung tissue and of inflammatory cytokines TNF-α, IL-1β, and IL-6, in both bronchoalveolar lavage fluid (BALF) and lung tissue in a dose-dependent manner (p < 0.01).Furthermore, AD increased the levels of p-Smad2/3. AD-induced increases in collagen I and α-SMA levels were reversed by loureirin B (LB). In addition, LB reduced AD-induced increased levels of the inflammatory cytokines TNF-α, IL-1β, and IL-6 in both bronchoalveolar lavage fluid (BALF) and lung tissue (p < 0.01).Conclusion: These results demonstrate that LB downregulates expression of fibrosis-related proteins and suppresses AD-induced PF. The mechanism responsible for the protective effect of LB on ADinduced PF might involve inhibition of the Smad2/3 pathway. Thus, LB is a potential therapeutic agent for the management of PF.
Keywords: Amiodarone, Loureirin B, Pulmonary fibrosis, Smad, Inflammatio
A Theory for Semantic Channel Coding With Many-to-one Source
As one of the potential key technologies of 6G, semantic communication is
still in its infancy and there are many open problems, such as semantic entropy
definition and semantic channel coding theory. To address these challenges, we
investigate semantic information measures and semantic channel coding theorem.
Specifically, we propose a semantic entropy definition as the uncertainty in
the semantic interpretation of random variable symbols in the context of
knowledge bases, which can be transformed into existing semantic entropy
definitions under given conditions. Moreover, different from traditional
communications, semantic communications can achieve accurate transmission of
semantic information under a non-zero bit error rate. Based on this property,
we derive a semantic channel coding theorem for a typical semantic
communication with many-to-one source (i.e., multiple source sequences express
the same meaning), and prove its achievability and converse based on a
generalized Fano's inequality. Finally, numerical results verify the
effectiveness of the proposed semantic entropy and semantic channel coding
theorem
- …