Most Simultaneous Localization and Mapping (SLAM) methods assume that environments are static. Such a strong assumption limits the application of most visual SLAM systems. The dynamic objects will cause many wrong data associations during the SLAM process. To address this problem, a novel visual SLAM method that follows the pipeline of feature-based methods called DM-SLAM is proposed in this paper. DM-SLAM combines an instance segmentation network with optical flow information to improve the location accuracy in dynamic environments, which supports monocular, stereo, and RGB-D sensors. It consists of four modules: semantic segmentation, ego-motion estimation, dynamic point detection and a feature-based SLAM framework. The semantic segmentation module obtains pixel-wise segmentation results of potentially dynamic objects, and the ego-motion estimation module calculates the initial pose. In the third module, two different strategies are presented to detect dynamic feature points for RGB-D/stereo and monocular cases. In the first case, the feature points with depth information are reprojected to the current frame. The reprojection offset vectors are used to distinguish the dynamic points. In the other case, we utilize the epipolar constraint to accomplish this task. Furthermore, the static feature points left are fed into the fourth module. The experimental results on the public TUM and KITTI datasets demonstrate that DM-SLAM outperforms the standard visual SLAM baselines in terms of accuracy in highly dynamic environments.

Hydrogen- and corrosion -related defects of pure iron and AISI 304 and 316 stainless steels were investigated by measuring Doppler broadening energy spectra of positron annihilation. The significant increase in S-parameter after corrosion in iron was interpreted as due to the formation of defects and voids during corrosion processes. However, a decrease of the S-parameter in AISI 304 and 316 stainless steels after corrosion was interpreted as dissolution of passivity oxide film. Furthermore, similar results in iron and stainless steels after hydrogen-charging were observed. Hydrogen damage between the surface and the bulk has a significant variation with depth. The larger increase in S-parameter near the surface after hydrogen-charging mainly comes from the formation of large-size defects (e.g. voids); however the increase in S-parameter in the bulk after hydrogen-charging mainly comes from the production of small-size defects.