ID 原文 译文
40916 针对单目视觉系统姿态估计精度不高的问题，提出了一种基于半直接法视觉里程计(SVO)的单目视觉惯性系统。 Aiming at the problem of low attitude estimation accuracy of monocular visual system, a monocular visual inertial system based on semi-direct visual odometry(SVO) is proposed.
40917 系统使用误差状态卡尔曼滤波器以松耦合方式对视觉信息和惯性导航信息进行融合。 The system uses an error-state Kalman filter to fuse visual information and inertial navigation information in a loosely coupled manner.
40918 视觉前端采用SVO处理图像数据，并修改了SVO的关键帧选择策略以及解决了相机前视场景中由于重定位失败导致姿态估计失败的问题。 SVO is used to process image data in the visual front-end, and the SVO keyframe selection strategy is modified to solve the problem of attitude estimation failure due to relocation failure in the front-view scene of the camera.
40919 使用EuRoc数据集中的MH 01子集对所提算法进行验证。 The proposed algorithm is validated by using the MH 01 subset of the EuRoc dataset.
40920 实验表明，基于SVO的单目视觉惯性系统能够有效的提高姿态估计精度。 The experiment shows that the monocular visual inertial system based on the SVO can effectively improve the accuracy of attitude estimation.
40921 相比单目SVO算法，所提算法的位置误差减少了13.4%，旋转角的误差减少了30%。 Compared with the monocular SVO algorithm, the position error of the proposed algorithm is reduced by 13.4%, the error of the rotation angle is reduced by 30%.
40922 针对现有去雾算法去雾效果不理想的问题，提出了一种基于注意力机制的单幅图像去雾算法。 Aiming at the problem that the existing defogging algorithm is not ideal for defogging, a single image defogging algorithm based on attention mechanism is proposed.
40923 通过引入注意力机制构建通道注意和像素注意，并将两者结合实现特征注意模块; Channel attention and pixel attention are constructed by introducing attention mechanism, and the feature attention module is realized by combining the two.
40924 再通过多尺度卷积、局部残差学习和特征注意搭建基本模块; Then the basic module is built through multi-scale convolution, local residual learning, and feature attention.
40925 最后结合全局残差学习实现端到端的去雾处理。 At last, the end-to-end defogging is realized by global residual learning.