分析了导航地图数据增量更新过程的时态特征,通过应用时态GIS实现了增量更新过程的建模,提出了基于历史跨度的时空数据模型(history span based temporal-spatial model,HSBTM)。在此模型的支持下,推导了增量数据的计算公式,论述了历史...分析了导航地图数据增量更新过程的时态特征,通过应用时态GIS实现了增量更新过程的建模,提出了基于历史跨度的时空数据模型(history span based temporal-spatial model,HSBTM)。在此模型的支持下,推导了增量数据的计算公式,论述了历史跨度时空数据模型的简化表达和实现方法。展开更多
This paper deals with the error analysis of a novel navigation algorithm that uses as input the sequence of images acquired from a moving camera and a Digital Terrain (or Elevation) Map (DTM/DEM). More specifically, i...This paper deals with the error analysis of a novel navigation algorithm that uses as input the sequence of images acquired from a moving camera and a Digital Terrain (or Elevation) Map (DTM/DEM). More specifically, it has been shown that the optical flow derived from two consecutive camera frames can be used in combination with a DTM to estimate the position, orientation and ego-motion parameters of the moving camera. As opposed to previous works, the proposed approach does not require an intermediate explicit reconstruction of the 3D world. In the present work the sensitivity of the algorithm outlined above is studied. The main sources for errors are identified to be the optical-flow evaluation and computation, the quality of the information about the terrain, the structure of the observed terrain and the trajectory of the camera. By assuming appropriate characterization of these error sources, a closed form expression for the uncertainty of the pose and motion of the camera is first developed and then the influence of these factors is confirmed using extensive numerical simulations. The main conclusion of this paper is to establish that the proposed navigation algorithm generates accurate estimates for reasonable scenarios and error sources, and thus can be effectively used as part of a navigation system of autonomous vehicles.展开更多
文摘分析了导航地图数据增量更新过程的时态特征,通过应用时态GIS实现了增量更新过程的建模,提出了基于历史跨度的时空数据模型(history span based temporal-spatial model,HSBTM)。在此模型的支持下,推导了增量数据的计算公式,论述了历史跨度时空数据模型的简化表达和实现方法。
文摘This paper deals with the error analysis of a novel navigation algorithm that uses as input the sequence of images acquired from a moving camera and a Digital Terrain (or Elevation) Map (DTM/DEM). More specifically, it has been shown that the optical flow derived from two consecutive camera frames can be used in combination with a DTM to estimate the position, orientation and ego-motion parameters of the moving camera. As opposed to previous works, the proposed approach does not require an intermediate explicit reconstruction of the 3D world. In the present work the sensitivity of the algorithm outlined above is studied. The main sources for errors are identified to be the optical-flow evaluation and computation, the quality of the information about the terrain, the structure of the observed terrain and the trajectory of the camera. By assuming appropriate characterization of these error sources, a closed form expression for the uncertainty of the pose and motion of the camera is first developed and then the influence of these factors is confirmed using extensive numerical simulations. The main conclusion of this paper is to establish that the proposed navigation algorithm generates accurate estimates for reasonable scenarios and error sources, and thus can be effectively used as part of a navigation system of autonomous vehicles.
