This paper presents an self-supervised deep learning network for monocular visual inertial odometry (named DeepVIO). DeepVIO provides absolute trajectory estimation by directly merging 2D optical flow feature (OFF) and Inertial Measurement Unit (IMU) data. Specifically, it firstly estimates the depth and dense 3D point cloud of each scene by using stereo sequences, and then obtains 3D geometric constraints including 3D optical flow and 6-DoF pose as supervisory signals. Note that such 3D optical flow shows robustness and accuracy to dynamic objects and textureless environments. In DeepVIO training, 2D optical flow network is constrained by the projection of its corresponding 3D optical flow, and LSTMstyle IMU preintegration network and the fusion network are learned by minimizing the loss functions from ego-motion constraints. Furthermore, we employ an IMU status update scheme to improve IMU pose estimation through updating the additional gyroscope and accelerometer bias. The experimental results on KITTI and EuRoC datasets show that DeepVIO outperforms state-of-the-art learning based methods in terms of accuracy and data adaptability. Compared to the traditional methods, DeepVIO reduces the impacts of inaccurate Camera-IMU calibrations, unsynchronized and missing data.

This paper presents a novel framework for simultaneously implementing localization and segmentation, which are two of the most important vision-based tasks for robotics. While the goals and techniques used for them were considered to be different previously, we show that by making use of the intermediate results of the two modules, their performance can be enhanced at the same time. Our framework is able to handle both the instantaneous motion and long-term changes of instances in localization with the help of the segmentation result, which also benefits from the refined 3D pose information.We conduct experiments on various datasets, and prove that our framework works effectively on improving the precision and robustness of the two tasks and outperforms existing localization and segmentation algorithms.

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Culture is the bloodline of the nation and the spiritual home of the people. Intangible cultural heritage (ICH) belongs to the field of culture, and the transmission of ICH is a kind of human-based cultural transmission, which is the shaping of people’s morality, character, sentiment, will, ideals and beliefs, value orientation, humanistic cultivation, artistic taste, way of thinking, wisdom, and ability in the practice of production and life of various ethnic groups. Based on the status acquisition model, this study analyzed the human capital (HC), social capital (SC), and psychological capital (PC) of ICH bearers from three perspectives. In addition to the conventional socio-demographic factors such as gender, age, place of residence, and education level as control variables, status attainment was introduced as a dependent variable, and occupational identity, job satisfaction, and entrepreneurial ability were introduced as mediating variables to construct a conceptual model in the hope of exploring the multiple influencing factors of status attainment among ICH bearers. Interviews were used to gain, the feelings and knowledge of experts, scholars, and ICH bearers, to lay a solid qualitative research foundation for this study. A questionnaire survey was also conducted to obtain basic information and professional experiences of ICH bearers to provide real support for the research analysis and discussion. As per the results of this research, all the hypotheses were supported except, HC did not have any significant impact on JS. Furthermore, SC was not in a significant association with career identity. Moreover, career identity and status attainment did not have a significant relationship.