首页|Findings from Yunnan Normal University Update Knowledge of Robotics (Ys-slam: Yo lact Plus Plus Based Semantic Visual Slam for Autonomous Adaptation To Dynamic E nvironments of Mobile Robots)
Findings from Yunnan Normal University Update Knowledge of Robotics (Ys-slam: Yo lact Plus Plus Based Semantic Visual Slam for Autonomous Adaptation To Dynamic E nvironments of Mobile Robots)
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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."
YunnanPeople's Republic of ChinaAsiaEmerging TechnologiesMachine LearningNano-robotRoboticsYunnan Normal U niversity
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