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Loop Closure Detection via Locality Preserving Matching With Global Consensus

IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 411-426页

作者： Jiayi Ma Kaining Zhang junjun jiang IEEE the Electronic Information School Wuhan UniversityWuhan 430072China the School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China

A critical component of visual simultaneous localization and mapping is loop closure detection(LCD),an operation judging whether a robot has come to a pre-visited ***,given a query image(i.e.,the latest view observed by the robot),it proceeds by first exploring images with similar semantic information,followed by solving the relative relationship between candidate pairs in the 3D *** this work,a novel appearance-based LCD system is ***,candidate frame selection is conducted via the combination of Superfeatures and aggregated selective match kernel(ASMK).We incorporate an incremental strategy into the vanilla ASMK to make it applied in the LCD *** is demonstrated that this setting is memory-wise efficient and can achieve remarkable *** dig up consistent geometry between image pairs during loop closure verification,we propose a simple yet surprisingly effective feature matching algorithm,termed locality preserving matching with global consensus(LPM-GC).The major objective of LPM-GC is to retain the local neighborhood information of true feature correspondences between candidate pairs,where a global constraint is further designed to effectively remove false correspondences in challenging sceneries,e.g.,containing numerous repetitive ***,we derive a closed-form solution that enables our approach to provide reliable correspondences within only a few *** performance of the proposed approach has been experimentally evaluated on ten publicly available and challenging *** show that our method can achieve better performance over the state-of-the-art in both feature matching and LCD *** have released our code of LPM-GC at https://***/jiayi-ma/LPM-GC.

关键词： Feature matching locality preserving matching loop closure detection SLAM

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Contrastive Learning for Blind Super-Resolution via A Distortion-Specific Network

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 78-89页

作者： Xinya Wang Jiayi Ma junjun jiang the Electronic Information School Wuhan UniversityWuhan 430072China IEEE the School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China

Previous deep learning-based super-resolution(SR)methods rely on the assumption that the degradation process is predefined(e.g.,bicubic downsampling).Thus,their performance would suffer from deterioration if the real degradation is not consistent with the *** deal with real-world scenarios,existing blind SR methods are committed to estimating both the degradation and the super-resolved image with an extra loss or iterative ***,degradation estimation that requires more computation would result in limited SR performance due to the accumulated estimation *** this paper,we propose a contrastive regularization built upon contrastive learning to exploit both the information of blurry images and clear images as negative and positive samples,*** regularization ensures that the restored image is pulled closer to the clear image and pushed far away from the blurry image in the representation ***,instead of estimating the degradation,we extract global statistical prior information to capture the character of the *** the coupling between the degradation and the low-resolution image,we embed the global prior into the distortion-specific SR network to make our method adaptive to the changes of *** term our distortion-specific network with contrastive regularization as *** extensive experiments on synthetic and realworld scenes demonstrate that our lightweight CRDNet surpasses state-of-the-art blind super-resolution approaches.

关键词： Blind super-resolution contrastive learning deep learning image super-resolution(SR)

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Modelling and Backstepping Motion Control of the Aircraft Skin Inspection Robot

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Computer Modeling in Engineering & Sciences 2019年第7期120卷 105-121页

作者： junjun jiang Congqing Wang College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjingChina

Aircraft skin health concerns whether the aircraft can fly *** this paper,an improved mechanical structure of the aircraft skin inspection robot was *** that the aircraft skin surface is a curved environment,we assume that the curved environment is equivalent to an inclined plane with a change in *** on this assumption,the Cartesian dynamics model of the robot is established using the Lagrange *** order to control the robot’s movement position accurately,a position backstepping control scheme for the aircraft skin inspection robot was *** to the dynamic model and taking into account the problems faced by the robot during its movement,a position constrained controller of the aircraft skin inspection robot is designed using the barrier Lyapunov *** at the disturbances in the robot,we adopt a fuzzy system to approximate the unknown dynamics related with system ***,the simulation results of the designed position constrained controller were compared with the sliding mode controller,and prove the validity of the position constrained controller.

关键词： Aircraft skin inspection robot dynamics modelling backstepping control fuzzy system barrier lyapunov function motion control

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Self-Supervised Monocular Depth Estimation via Discrete Strategy and Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1307-1310页

作者： Zhenyu Li junjun jiang Xianming Liu School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001 Peng Cheng Laboratory Shenzhen 518052China

Dear Editor,This letter is concerned with self-supervised monocular depth *** estimate uncertainty simultaneously,we propose a simple yet effective strategy to learn the uncertainty for self-supervised monocular depth estimation with the discrete strategy that explicitly associates the prediction and the uncertainty to train the ***,we propose the uncertainty-guided feature fusion module to fully utilize the uncertainty *** will be available at https://***/zhyever/***-supervised monocular depth estimation methods turn into promising alternative trade-offs in both the training cost and the inference ***,compound losses that couple the depth and the pose lead to a dilemma of uncertainty calculation that is crucial for critical safety *** solve this issue,we propose a simple yet effective strategy to learn the uncertainty for self-supervised monocular depth estimation using the discrete bins that explicitly associate the prediction and the uncertainty to train the *** strategy is more pluggable without any additional changes to self-supervised training losses and improves model ***,to further exert the uncertainty information,we propose the uncertainty-guided feature fusion module to refine the depth estimation.

关键词： estimation utilize uncertainty

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Fully 1×1 Convolutional Network for Lightweight Image Super-resolution

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Machine Intelligence Research 2024年第6期21卷 1062-1076页

作者： Gang Wu junjun jiang Kui jiang Xianming Liu Faculty of Computing Harbin Institute of TechnologyHarbin150001China

Deep convolutional neural networks,particularly large models with large kernels(3x3 or more),have achieved significant progress in single image super-resolution(SISR)***,the heavy computational footprint of such models prevents their de-ployment in real-time,resource-constrained ***,1×1 convolutions have substantial computational efficiency,but struggle with aggregating local spatial representations,which is an essential capability for SISR *** response to this dichotomy,we propose to harmonize the merits of both 3x3 and 1×1 kernels,and exploit their great potential for lightweight SISR ***-ally,we propose a simple yet effective fully 1×1 convolutional network,named shift-Conv-based network(SCNet).By incorporating a parameter-free spatial-shift operation,the fully 1×1 convolutional network is equipped with a powerful representation capability and impressive computational *** experiments demonstrate that SCNets,despite their fully 1×1 convolutional structure,consistently match or even surpass the performance of existing lightweight SR models that employ regular *** code and pretrained models can be found at .

关键词： Lightweight network image super-resolution convolutional neural network transformer image restoration

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BaMBNet:A Blur-Aware Multi-Branch Network for Dual-Pixel Defocus Deblurring

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 878-892页

作者： Pengwei Liang junjun jiang Xianming Liu Jiayi Ma School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China Electronic Information School Wuhan UniversityWuhan 430072China IEEE

Reducing the defocus blur that arises from the finite aperture size and short exposure time is an essential problem in computational *** is very challenging because the blur kernel is spatially varying and difficult to estimate by traditional *** to its great breakthrough in low-level tasks,convolutional neural networks(CNNs)have been introdu-ced to the defocus deblurring problem and achieved significant ***,previous methods apply the same learned kernel for different regions of the defocus blurred images,thus it is difficult to handle nonuniform blurred *** this end,this study designs a novel blur-aware multi-branch network(Ba-MBNet),in which different regions are treated *** particular,we estimate the blur amounts of different regions by the internal geometric constraint of the dual-pixel(DP)data,which measures the defocus disparity between the left and right *** on the assumption that different image regions with different blur amounts have different deblurring difficulties,we leverage different networks with different capacities to treat different image ***,we introduce a meta-learning defocus mask generation algorithm to assign each pixel to a proper *** this way,we can expect to maintain the information of the clear regions well while recovering the missing details of the blurred *** quantitative and qualitative experiments demonstrate that our BaMBNet outperforms the state-of-the-art(SOTA)*** the dual-pixel defocus deblurring(DPD)-blur dataset,the proposed BaMBNet achieves 1.20 dB gain over the previous SOTA method in term of peak signal-to-noise ratio(PSNR)and reduces learnable parameters by 85%.The details of the code and dataset are available at https://***/junjun-jiang/BaMBNet.

关键词： Blur kernel convolutional neural networks(CNNs) defocus deblurring dual-pixel(DP)data meta-learning

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Loop Closure Detection With Reweighting Net VLAD and Local Motion and Structure Consensus

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IEEE/CAA Journal of Automatica Sinica 2022年第6期9卷 1087-1090页

作者： Kaining Zhang Jiayi Ma junjun jiang Electronic Information School Wuhan UniversityWuhan 430072China IEEE School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China

Dear Editor,Loop closure detection(LCD)is an important module in simultaneous localization and mapping(SLAM).In this letter,we address the LCD task from the semantic aspect to the geometric *** this end,a network termed as AttentionNetVLAD which can simultaneously extract global and local features is *** leverages attentive selection for local features,coupling with reweighting the soft assignment in NetVLAD via the attention map for global *** a query image,candidate frames are first identified coarsely by retrieving similar global features in the database via hierarchical navigable small world(HNSW).As global features mainly summarize the semantic information of images and lead to compact representation,information about spatial arrangement of visual elements is lost.

关键词： Net hierarchical arrangement

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Image Enhancement via Associated Perturbation Removal and Texture Reconstruction Learning

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2253-2269页

作者： Kui jiang Ruoxi Wang Yi Xiao junjun jiang Xin Xu Tao Lu IEEE the School of Computer Science and Technology Harbin Institute of Technology Zhengzhou Research Institute Harbin Institute of Technology the School of Artificial Intelligence Jianghan University the School of Geodesy and Geomatics Wuhan University the Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology the School of Computer Science and Engineering and also with the Hubei Province Key Laboratory of Intelligent Robot Wuhan Institute of Technology

Degradation under challenging conditions such as rain, haze, and low light not only diminishes content visibility, but also results in additional degradation side effects, including detail occlusion and color distortion. However, current technologies have barely explored the correlation between perturbation removal and background restoration, consequently struggling to generate high-naturalness content in challenging scenarios. In this paper, we rethink the image enhancement task from the perspective of joint optimization: Perturbation removal and texture reconstruction. To this end, we advise an efficient yet effective image enhancement model, termed the perturbation-guided texture reconstruction network(PerTeRNet). It contains two subnetworks designed for the perturbation elimination and texture reconstruction tasks, respectively. To facilitate texture recovery,we develop a novel perturbation-guided texture enhancement module(PerTEM) to connect these two tasks, where informative background features are extracted from the input with the guidance of predicted perturbation priors. To alleviate the learning burden and computational cost, we suggest performing perturbation removal in a sub-space and exploiting super-resolution to infer high-frequency background details. Our PerTeRNet has demonstrated significant superiority over typical methods in both quantitative and qualitative measures, as evidenced by extensive experimental results on popular image enhancement and joint detection tasks. The source code is available at https://***/kuijiang94/PerTeRNet.

关键词： Association learning attention mechanism image enhancement perturbation modeling

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DepthFormer:Exploiting Long-range Correlation and Local Information for Accurate Monocular Depth Estimation

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Machine Intelligence Research 2023年第6期20卷 837-854页

作者： Zhenyu Li Zehui Chen Xianming Liu junjun jiang Faculty of Computing Harbin Institute of TechnologyHarbin150001China Department of Automation University of Science and Technology of ChinaHefei230026China

This paper aims to address the problem of supervised monocular depth *** start with a meticulous pilot study to demonstrate that the long-range correlation is essential for accurate depth ***,the Transformer and convolution are good at long-range and close-range depth estimation,***,we propose to adopt a parallel encoder architecture consisting of a Transformer branch and a convolution *** former can model global context with the effective attention mechanism and the latter aims to preserve the local information as the Transformer lacks the spatial inductive bias in modeling such ***,independent branches lead to a shortage of connections between *** bridge this gap,we design a hierarchical aggregation and heterogeneous interaction module to enhance the Transformer features and model the affinity between the heterogeneous features in a set-to-set translation *** to the unbearable memory cost introduced by the global attention on high-resolution feature maps,we adopt the deformable scheme to reduce the *** experiments on the KITTI,NYU,and SUN RGB-D datasets demonstrate that our proposed model,termed DepthFormer,surpasses state-of-the-art monocular depth estimation methods with prominent *** effectiveness of each proposed module is elaborately evaluated through meticulous and intensive ablation studies.

关键词： Autonomous driving 3D reconstruction monocular depth estimation Transformer convolution

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MAUN:Memory-Augmented Deep Unfolding Network for Hyperspectral Image Reconstruction

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1139-1150页

作者： Qian Hu Jiayi Ma Yuan Gao junjun jiang Yixuan Yuan Electronic Information School Wuhan UniversityWuhan 430072China School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China Department of Electronic Engineering Chinese University of Hong KongHong Kong 999077China

Spectral compressive imaging has emerged as a powerful technique to collect the 3D spectral information as 2D *** algorithm for restoring the original 3D hyperspectral images(HSIs)from compressive measurements is pivotal in the imaging *** approaches painstakingly designed networks to directly map compressive measurements to HSIs,resulting in the lack of interpretability without exploiting the imaging *** some recent works have introduced the deep unfolding framework for explainable reconstruction,the performance of these methods is still limited by the weak information transmission between iterative *** this paper,we propose a Memory-Augmented deep Unfolding Network,termed MAUN,for explainable and accurate HSI ***,MAUN implements a novel CNN scheme to facilitate a better extrapolation step of the fast iterative shrinkage-thresholding algorithm,introducing an extra momentum incorporation step for each iteration to alleviate the information ***,to exploit the high correlation of intermediate images from neighboring iterations,we customize a cross-stage transformer(CSFormer)as the deep denoiser to simultaneously capture self-similarity from both in-stage and cross-stage features,which is the first attempt to model the long-distance dependencies between iteration *** experiments demonstrate that the proposed MAUN is superior to other state-of-the-art methods both visually and *** code is publicly available at https://***/HuQ1an/MAUN.

关键词： Deep folding iteration

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