Yingping Huang
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Enhanced visual-inertial SLAM Using SuperPoint and semantic geometric dynamic feature detection
Abstract Visual–inertial simultaneous localization and mapping (VI-SLAM) is fundamental for unmanned driving and VR. However, traditional feature-based SLAM systems rely on hand-crafted features and lack dedicated methods for handling dynamic objects, which results in degraded performance under challenging conditions, such as violent motion, varying illumination, and dynamic environments.
Frontiers | How future work self-salience influences occupational sense of mission among medical students in the post-pandemic era: a dual-perspective analysis from variable-centered and person-centered approaches based on professional identity
School of Medical Imaging, North Sichuan Medical College, Nanchong, China Background: In the post-pandemic era, medical students face increased occupational uncertainty. Future work self-salience as a prospective dimension of self-awareness, shapes career choices and constitutes a core driver of professional engagement. However, few studies have explored the relationship between future work self-salience and occupational sense of mission among medical students or its underlying mechanisms.
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