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Recently, with the broad applications of deep learning technology in image denoising, many deep neural networks based on the residual block (ResBlock) have been proposed to improve the performance. This paper presents DC-ResBlock, a ResBlock with an extra dilated convolution, to replace standard ResBlock for image denoising. Our study shows that the deep neural network with DC-ResBlocks, named DRNet, can achieve a very competitive result. We experiment with other residual blocks by adding Rectified Linear Unit, batch normalization, and Dilated Convolution to the standard ResBlock in several different ways. Ablation study shows that DC-Resblock has the best denoising performance. To evaluate the robustness of proposed DRNet, we make a statistical measure using many different random seeds instead of just a single seed, as used by many previous studies. DRNet performs well in denoising gray and color images with additive white Gaussian noise. Furthermore, because of its good performance on the Smartphone Image Denoising Dataset, DRNet is hopeful to be applied in practical tasks. The code of DRNet is accessible at https://github.com/JiaHongZ/DRNet.

Most of the existing simultaneous localization and mapping (SLAM) methods are based on the static environment assumption. The presence of moving objects in the scene will lead to much uncertainty in SLAM results, which also hinders the loop-closure detection (LCD). Although moving object tracking (MOT) is necessary for planning and decisions, it is often accomplished separately. To jointly solve SLAM and MOT for complex urban driving scenarios, this paper presents a high performance method named 4D-SLAM based on the fusion of LiDAR and IMU. The integration of SLAM and MOT is formulated as a joint posterior probability problem based on a dynamic Bayesian network (DBN) and is implemented with the following four sequential stages: preprocessing, moving object detection and tracking, odometry estimation and mapping. In the preprocessing stage, the motion distortion uncertainty caused by LiDAR scanning is first compensated for, and the initial LiDAR motion is estimated. In the moving object detection and tracking stage, we exploit a CNN-based segmentation network to detect the potential moving objects first, then the states of the potential moving objects are optimized by an unscented Kalman filter (UKF). In the odometry estimation stage, the distinctive planar and edge features extracted from a static background point cloud are used for the odometry estimation, and a two-step Levenberg-Marquardt optimization method is adopted to solve the 6DOF pose across consecutive scans. In the mapping stage, the mapping based on the pose estimation result and LCD are realized, and graph-based global optimization is exploited to further improve the map consistency for large scale environment. The comprehensive experiments with the open source dataset KITTI and the data collected by us show that the presented method not only outperforms the SOTA SLAM methods in terms of trajectory and mapping accuracy but can also detect and track moving objects efficiently.

The uncertainty of worn parts is a challenge for the remanufacturing process. Therefore, an integrated optimization decision method for remanufacturing process based on conditional evidence theory under uncertainty is proposed. On the basis of production history data of remanufacturing enterprises, prior information of the remanufacturing process is generated, and the prior evidence is constructed. Then, depending on the relationship between the parameters and the processing technology, the information of detection and evaluation of parts' characteristic parameters is transformed into evidence. The prior evidence and the evidence are fused corresponding to the parameter value. Then, the influence law among production data, characteristics of worn parts and technological process in remanufacturing is revealed. The fusion result has a one-to-one correspondence with the processing technology of worn parts. According to the decision rules, the optimal processing technology of worn parts can be obtained by judging the fusion results. The statistical data of remanufacturing worn crankshafts shows that the quality improved by 2.5%, the production cost reduced by 5.6%, and the time saved by 7.3%. This study provides theoretical and methodological support for the optimization of remanufacturing production.