变电站室内无人机巡检可有效降低人工巡检作业强度。由于飞行精度要求高,搭载能力有限,仅依靠无人机搭载摄像头与惯性测量单元(inertial measurement unit, IMU)数据融合确定位姿无法满足精度要求,为此,提出基于变电站室内已有固定摄像...变电站室内无人机巡检可有效降低人工巡检作业强度。由于飞行精度要求高,搭载能力有限,仅依靠无人机搭载摄像头与惯性测量单元(inertial measurement unit, IMU)数据融合确定位姿无法满足精度要求,为此,提出基于变电站室内已有固定摄像头的泛在物联的多视觉-惯导融合框架,针对室内光线情况对无人机摄像头图像进行强化,并与IMU数据结合得到初步的无人机位置数据。进一步通过在无人机上布设二维码(quick response code,QR码),应用改进后的PnP(perspective-n-point)算法优化无人机位姿数据。飞行结束后在无人机机巢对IMU的累计误差进行校验。实验证明:该方法布设与维护的工作量小,相较仅依靠搭载摄像头与IMU数据融合算法,飞行精度有较大提高,可满足变电站内无人机巡检作业的需要。展开更多
With the development of global position system（GPS）,wireless technology and location aware services,it is possible to collect a large quantity of trajectory data.In the field of data mining for moving objects,the pr...With the development of global position system（GPS）,wireless technology and location aware services,it is possible to collect a large quantity of trajectory data.In the field of data mining for moving objects,the problem of anomaly detection is a hot topic.Based on the development of anomalous trajectory detection of moving objects,this paper introduces the classical trajectory outlier detection（TRAOD） algorithm,and then proposes a density-based trajectory outlier detection（DBTOD） algorithm,which compensates the disadvantages of the TRAOD algorithm that it is unable to detect anomalous defects when the trajectory is local and dense.The results of employing the proposed algorithm to Elk1993 and Deer1995 datasets are also presented,which show the effectiveness of the algorithm.展开更多
基金supported by the Aeronautical Science Foundation of China(20111052010)the Jiangsu Graduates Innovation Project (CXZZ120163)+1 种基金the "333" Project of Jiangsu Provincethe Qing Lan Project of Jiangsu Province
文摘With the development of global position system（GPS）,wireless technology and location aware services,it is possible to collect a large quantity of trajectory data.In the field of data mining for moving objects,the problem of anomaly detection is a hot topic.Based on the development of anomalous trajectory detection of moving objects,this paper introduces the classical trajectory outlier detection（TRAOD） algorithm,and then proposes a density-based trajectory outlier detection（DBTOD） algorithm,which compensates the disadvantages of the TRAOD algorithm that it is unable to detect anomalous defects when the trajectory is local and dense.The results of employing the proposed algorithm to Elk1993 and Deer1995 datasets are also presented,which show the effectiveness of the algorithm.
