摘要
由一名新闻记者-机器人与机器学习每日新闻编辑-调查人员讨论机器人学的新发现。针对移动机器人对动态环境的自主适应性差的问题,本文提出了一种基于YOL ACT++的语义视觉SLAM,用于移动机器人对动态环境的自主适应。首先,利用轻量级的YOLACT++检测和识别潜在的动态对象;结合Mahalanobis距离去除主动动态目标上的特征点，并利用极线约束和聚类技术去除被动动态目标上的特征点。本研究经费来源于国家自然科学基金(NSFC)。新闻记者引用了云南师范大学的研究成果，然后根据动态成分和静态成分的语义标签，将全局语义图分为三个部分进行构建，选择扫描重叠和匀速运动模型跟踪运动对象，并将动态成分添加到背景图中。"构建了一个与实际环境一致并实时更新的三维语义octr ee图."
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Robotics. According to news reporting from Yunnan, People's Republic of China, b y NewsRx journalists, research stated, "Aiming at the problem of poor autonomous adaptability of mobile robots to dynamic environments, this paper propose a YOL ACT++ based semantic visual SLAM for autonomous adaptation to dynamic environmen ts of mobile robots. First, a light-weight YOLACT++ is utilized to detect and se gment potential dynamic objects, and Mahalanobis distance is combined to remove feature points on active dynamic objects, also, epipolar constraint and clusteri ng are employed to eliminate feature points on passive dynamic objects." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). The news correspondents obtained a quote from the research from Yunnan Normal Un iversity, "Then, in terms of the semantic labels of dynamic and static component s, the global semantic map is divided into three parts for construction. The sem antic overlap and uniform motion model are chose to track moving objects and the dynamic components are added to the background map. Finally, a 3D semantic octr ee map is constructed that is consistent with the real environment and updated i n real time."
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