首页|Recent Findings in Robotics Described by Researchers from Xi'an Jiaotong Univers ity (Panoramic Visual System for Spherical Mobile Robots)
Recent Findings in Robotics Described by Researchers from Xi'an Jiaotong Univers ity (Panoramic Visual System for Spherical Mobile Robots)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Research findings on Robotics are discussed in a new report. According to news originating from Xi'an, People's Republic of China, by NewsRx correspondents, research stated, "Aimed at the challenges of wide-an gle mobile robot visual perception for diverse field applications, we present th e spherical robot visual system that uses a 360 degrees field of view (FOV) for realizing real-time object detection. The spherical robot image acquisition syst em model is developed with optimal parameters, including camera spacing, camera axis angle, and the distance of the target image plane." Funders for this research include National Natural Science Foundation of China ( NSFC), Shanxi Provincial Key Research Project, Xinjiang Funded by Autonomous Reg ion Major Science and Technology Special Project, Shaanxi Provincial Key RD Prog ram, Fundamental Research Funds for the Central Universities. Our news journalists obtained a quote from the research from Xi'an Jiaotong Univ ersity, "Two 180 $ <. > {\circ}$ -wide panoramic FOVs, front and rear view, are formed using four on-board cameras. Th e speed of the SURF algorithm is increased for feature extraction and matching. For seamless fusion of the images, an improved fade-in and fade-out algorithm is used, which not only improves the seam quality but also improves object detecti on performance. The speed of the dynamic image stitching is significantly enhanc ed by using a cache-based sequential image fusion method. On top of the acquired panoramic wide FOVs, the YOLO algorithm is used for real-time object detection. "
Xi'anPeople's Republic of ChinaAsiaAlgorithmsEmerging TechnologiesMachine LearningNano-robotRobotRobotic sXi'an Jiaotong University
NETL
NSTL
