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

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

Learning Modal-Invariant and Temporal-Memory for Video-based Visible-Infrared Person Re-Identification

Thanks for the cross-modal retrieval techniques, visible-infrared (RGB-IR) person re-identification (Re-ID) is achieved by projecting them into a common space, allowing person Re-ID in 24-hour surveillance systems. However, with respect to the probe-to-gallery, almost all existing RGB-IR based cross-modal person Re-ID methods focus on image-to-image matching, while the video-to-video matching which contains much richer spatial- and temporal-information remains under-explored. In this paper, we primarily study the video-based cross-modal person Re-ID method. To achieve this task, a video-based RGB-IR dataset is constructed, in which 927 valid identities with 463,259 frames and 21,863 tracklets captured by 12 RGB/IR cameras are collected. Based on our constructed dataset, we prove that with the increase of frames in a tracklet, the performance does meet more enhancement, demonstrating the significance of video-to-video matching in RGB-IR person Re-ID. Additionally, a novel method is further proposed, which not only projects two modalities to a modal-invariant subspace, but also extracts the temporal-memory for motion-invariant. Thanks to these two strategies, much better results are achieved on our video-based cross-modal person Re-ID. The code and dataset are released at: https://github.com/VCMproject233/MITML

Third-order nonlinearity in Ge–Sb–Se glasses at mid-infrared wavelengths

International audienceThe optical properties of Ge–Sb–Se glasses have been extensively studied at telecom wavelengths in recent years. However, the understanding of nonlinearity in Ge–Sb–Se glasses at mid-infrared wavelengths still remains limited. In this work, a series of Ge20SbxSe80−x (x = 0, 5, 10) glasses were prepared by conventional melt–quenching method. The absorption spectra and the refractive index of glasses were recorded. The third order nonlinearity, n2, and nonlinear absorption coefficient were measured for Ge–Sb–Se glass samples at the wavelengths of 1550, 2000 and 2500 nm by Z-scan technique, respectively. With the increasing of Sb contents, the linear refractive index of glass increased. Among the three operating wavelengths, all the three glass samples have a highest n2 at 2000 nm. By using the figure of merit (FOM) to evaluate the studied three glasses, the Ge20Sb10Se70 glass shows the greatest potential for mid-IR all optical switching device