最新刊期

    32 3 2024

      Information Sciences

    • MA Qinglu,WANG Junhao,ZHANG Jie,ZOU Zheng
      Vol. 32, Issue 3, Pages: 422-434(2024) DOI: 10.37188/OPE.20243203.0422
      摘要:To address the issues of inaccurate mapping and position drift in 3D autonomous driving maps, LIDAR odometry was utilized to counteract cumulative errors of the inertial measurement unit(IMU), and corrections for LIDAR point cloud distortions were made through IMU pre-integration. This approach enabled the creation of a mapping system where LIDAR and IMU were tightly integrated. Subsequently, the back-end map was enhanced by the incorporation of loopback detection, LIDAR odometry, and IMU pre-integration factors, aiming to bolster the global consistency of the positioning map and minimize cumulative drift errors. The optimization of the back-end map sought to enhance global localization consistency, reduce positioning errors, and curtail cumulative drift. Experimental validation was conducted in a school campus environment and with the use of the KITTI open-source dataset. The results demonstrate that in the school campus scenario, an 11.04% reduction in average APE error and a 17.35% decrease in RMSE are achieved by the refined algorithm compared to the baseline algorithm. For the KITTI dataset scenario, a reduction of 10.04% in both average APE error and RMSE, and a 12.04% decrease in mean square error are observed, underscoring the efficacy of the enhanced mapping technique in elevating position estimation and map construction precision.  
      关键词:lidar;automatic driving;synchronous positioning and mapping;sensor fusion   
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      发布时间:2024-02-22
    • WANG Wei,FU Feiya,LEI Hao,TANG Zili
      Vol. 32, Issue 3, Pages: 435-444(2024) DOI: 10.37188/OPE.20243203.0435
      摘要:In visible and thermal infrared tracking(RGB-T), to effectively merge these two modalities building on traditional tracking techniques, this study introduces an attention-based RGB-T tracking approach based on the attention mechanism. This method employs the attention mechanism to augment and integrate features from both visible and infrared images. It features a self-feature enhancement encoder to boost single modality features, and a cross-feature interaction decoder for merging the enhanced features from both modalities. Both the encoder and decoder incorporate dual layers of attention modules. To streamline the network, the traditional attention module is simplified by substituting fully connected layers with 1×1 convolutions. Moreover, it merges features from various convolutional layers to thoroughly explore details and semantic insights. Comparative experiments on three datasets—GTOT, RGBT234, and LasHeR—demonstrate that our method achieves superior tracking performance, underscoring the efficacy of the attention mechanism in RGB-T tracking.  
      关键词:RGB-T tracking;attention mechanism;feature fuse of multi-modality;feature enhancement   
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      发布时间:2024-02-22
    • ZHANG Jingjing,DU Xingzhuo,ZHI Shuai,DING Guopeng
      Vol. 32, Issue 3, Pages: 445-455(2024) DOI: 10.37188/OPE.20243203.0445
      摘要:To address the challenges of large-scale and complex network structures in deep learning-based stereo matching, this work introduces a compact yet highly accurate network. The feature extraction module simplifies by removing complex, redundant residual layers and incorporating an Atrous Spatial Pyramid Pooling (ASPP) module to broaden the field of view and enhance contextual information extraction. For cost calculation, three-dimensional (3D) convolutional layers refine stereo matching accuracy through cost aggregation. In addition, a bilateral grid module is integrated into the cost aggregation process, achieving precise disparity maps with reduced resolution demands. Tested on widely-used datasets like KITTI 2015 and Scene Flow, our network demonstrates a significant reduction in parameters by approximately 38% compared to leading networks like Pyramid Stereo Matching Network (PSM-Net), without compromising on experimental accuracy. Notably, it achieves an end-point error (EPE) of 0.86 on the Scene Flow dataset, outperforming many top-performing networks. Thus, our network effectively balances speed and accuracy in stereo matching.  
      关键词:computer vision;stereo matching;artificial neural network;parallax   
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      发布时间:2024-02-22
    • ZHOU Tao,YU Mei,CHEN Yeyao,JIANG Zhidi,JIANG Gangyi
      Vol. 32, Issue 3, Pages: 456-465(2024) DOI: 10.37188/OPE.20243203.0456
      摘要:Light field cameras, capable of capturing both the intensity and direction of light, are utilized in applications like foreground occlusion removal and depth estimation. However, the limited size of imaging sensors restricts the simultaneous achievement of high spatial and angular resolution in light field images. This paper introduces a method combining Fourier convolution and channel attention for light field angular reconstruction, which indirectly creates dense light field images from sparse ones using reference views from the image's four corners. This method leverages the light field image's inherent 4D structure, employing channel⁃level dense fast Fourier residual convolution blocks to model the spatial and angular correlations in both spatial and frequency domains. Channel attention blocks, utilizing global response normalization, then adaptively fuse these channels. Furthermore, an enhanced viewpoint⁃weighted indirect synthesis approach is proposed, assigning a confidence map to each reference view to improve the synthesis of new, realistic views by establishing relationships between reference views. Experimental results demonstrate that our method outperforms the advanced light field angular reconstruction technique IRVAE, showing an average PSNR improvement of 0.08, 0.13, and 0.13 dB on the natural light field dataset 30Scenes, Occlusion, and Reflective, respectively, ensuring clear reconstruction results with angular consistency in the light field.  
      关键词:light field angular reconstruction;fourier convolution;global response normalization;viewpoint weighting indirect view synthesis   
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      发布时间:2024-02-22
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