本文主要介绍了SURFER图件到M APG IS图件的转换,使用M APG IS软件中的图象转换、控制点文件及图件“误差校正”等功能绘制激电测深断面图的详细方法和步骤,解决了剖面地形起伏不平和AB/2供电极距不等间距情况下计算机制作激电测深断面...本文主要介绍了SURFER图件到M APG IS图件的转换,使用M APG IS软件中的图象转换、控制点文件及图件“误差校正”等功能绘制激电测深断面图的详细方法和步骤,解决了剖面地形起伏不平和AB/2供电极距不等间距情况下计算机制作激电测深断面图方面的难题。展开更多
Terrain referenced navigation estimates an aircraft navigation status by utilizing a radar altimeter measuring a distance between the aircraft and terrain elevation. Accurate digital elevation map is essential to esti...Terrain referenced navigation estimates an aircraft navigation status by utilizing a radar altimeter measuring a distance between the aircraft and terrain elevation. Accurate digital elevation map is essential to estimate the aircraft states correctly. However, the elevation map cannot represent the real terrain perfectly and there exists map error between the estimated and the true maps. In this paper, an influence of the map error on measurement equation is analyzed and a technique to incorporate the error in the filter is proposed. The map error is divided into two sources, accuracy error and resolution error. The effectiveness of the suggested technique is verified by simulation results. The method modifies a sensor noise covariance only so there is no additional computational burden from the conventional filter.展开更多
SLAM is one of the most important components in robot navigation. A SLAM algorithm based on image sequences captured by a single digital camera is proposed in this paper. By this algorithm, SIFT feature points are sel...SLAM is one of the most important components in robot navigation. A SLAM algorithm based on image sequences captured by a single digital camera is proposed in this paper. By this algorithm, SIFT feature points are selected and matched between image pairs sequentially. After three images have been captured, the environment’s 3D map and the camera’s positions are initialized based on matched feature points and intrinsic parameters of the camera. A robust method is applied to estimate the position and orientation of the camera in the forthcoming images. Finally, a robust adaptive bundle adjustment algorithm is adopted to optimize the environment’s 3D map and the camera’s positions simultaneously. Results of quantitative and qualitative experiments show that our algorithm can reconstruct the environment and localize the camera accurately and efficiently.展开更多
文摘Terrain referenced navigation estimates an aircraft navigation status by utilizing a radar altimeter measuring a distance between the aircraft and terrain elevation. Accurate digital elevation map is essential to estimate the aircraft states correctly. However, the elevation map cannot represent the real terrain perfectly and there exists map error between the estimated and the true maps. In this paper, an influence of the map error on measurement equation is analyzed and a technique to incorporate the error in the filter is proposed. The map error is divided into two sources, accuracy error and resolution error. The effectiveness of the suggested technique is verified by simulation results. The method modifies a sensor noise covariance only so there is no additional computational burden from the conventional filter.
基金Project supported by the National Natural Science Foundation of China (Nos. 60534070 and 60502006)the Science and Tech-nology Department of Zhejiang Province (No. 2005C14008), China
文摘SLAM is one of the most important components in robot navigation. A SLAM algorithm based on image sequences captured by a single digital camera is proposed in this paper. By this algorithm, SIFT feature points are selected and matched between image pairs sequentially. After three images have been captured, the environment’s 3D map and the camera’s positions are initialized based on matched feature points and intrinsic parameters of the camera. A robust method is applied to estimate the position and orientation of the camera in the forthcoming images. Finally, a robust adaptive bundle adjustment algorithm is adopted to optimize the environment’s 3D map and the camera’s positions simultaneously. Results of quantitative and qualitative experiments show that our algorithm can reconstruct the environment and localize the camera accurately and efficiently.
