GIMPHI: a new integration approach for early impact assessment
GIMPHI: a new integration approach for early impact assessment
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GIMPHI：早期影响评估的新集成方法
DOI：
10.1007/s12518-011-0069-6
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发表时间：
2011
影响因子：
2.7
通讯作者：
M. Piras
中科院分区：
文献类型：
--
作者：
M. De Agostino;A. Lingua;D. Marenchino;F. Nex;M. Piras
在过去的2年中，Politecnico di都灵的地理信息学研究小组开发了一种低成本的移动的测绘系统，其中只涉及低成本的传感器。该系统配备了网络摄像头、多达三个MEMS IMU和多达四个GNSS接收器。在此开发过程中，为了在数据处理后获得良好的质量，解决了几个（不可忽略的）问题。低成本系统的主要问题之一涉及全球导航卫星系统中断的发生。在这种情况下，IMU只能估计短时间内车辆的轨迹和姿态。出于这个原因，考虑到大量的帧可用（约5-10帧每秒，fps），基于视觉的导航（VBN）的方法，称为GIMPHI（GNSS IMU和PHotogrammetry集成），已经实现和测试。利用一种新的自适应尺度不变特征变换（A²SIFT）算法，从非常规几何配置（大仿射变换和旋转）获取的图像序列中提取连接点，并对大面积纹理不良区域进行匹配。全球导航卫星系统/惯性测量装置导航解决方案已通过与摄影测量方法（光束法区域网平差）相结合的方式加以改进，并采用了严格的加权矩阵，以便考虑到各种传感器观测（全球导航卫星系统、惯性测量装置和图像）的不同准确度。本文详细描述了这种综合方法。第一次测试和取得的结果，然后显示，以评估所提出的方法的可靠性。
Over the last 2 years, the Geomatics Research Group at the Politecnico di Torino has developed a low cost mobile mapping system, in which only low cost sensors are involved. The system is equipped with webcams, up to three MEMS IMU and up to four GNSS receivers. During this development, several (non negligible) problems have been solved in order to obtain good quality after the data processing. One of the main problems of the low cost systems concerns the occurrence of GNSS outages. In this case, the IMU can only estimate the trajectory and the attitude of the vehicle for short periods. For this reason, considering the high number of frames available (about 5–10 frames per second, fps), a vision-based navigation (VBN) approach, called GIMPHI (GNSS IMU and PHotogrammetry Integration), has been realized and tested. Using a novel auto-adaptive scale-invariant feature transform (A²SIFT) algorithm, tie points are extracted from sequences of images acquired according to unconventional geometric configuration (large affine transformation and rotations) andzz with large bad-textured areas. The GNSS/IMU navigation solutions have been refined by means of integration with a photogrammetric approach (bundle block adjustment) and a rigorous weight matrix has been adopted in order to consider the different accuracies of the various sensor observations (GNSS, IMU and images). A detailed description of this integrated approach is presented in this paper. The first tests and the achieved results are then shown in order to evaluate the reliability of the proposed approach.