2-DIMENSIONAL GEOMETRIC ANALYSIS OF A SIMPLE FREE NETWORK

This paper attempts to quantify geometric considerations in observations and observe trends in solutions to free network solutions. The method of investigation will be utilizing 2D observations to determine how each measurement affects the overall solution and the location of the observations relative to the other nodes. A local reference system will be determined using the Gauss-Markov model with constraints by fixing the largest range observation to the y-axis to give a relative orientation. Further solutions will be calculated by fixing additional points to generate multiple least squares solutions relative to the local reference system. The resulting final points will be modeled using the Gauss-mixture model and compared to a simulated dataset generated by adding random error to the observations. Different weight matrices will be tested to demonstrate the effect on the overall solution. These methods were chosen because of prior experimentation by different research groups studying geometric considerations for UAS and ground surveying conditions. The major contribution will be the trends observed in the modeling and the correlation of the fixed local solutions to the geometry of the points.

Extraction method of position and posture information of robot arm picking up target based on RGB-D data

There is a big error in the traditional method to extract the position and attitude information of the robot. In the process of obtaining the target attitude, a method of extracting the target attitude information of robot arm based on RGB-D data is proposed. The position and attitude of the manipulator target are acquired by depth image processing, and the detected target position is sent to the manipulator control node, and the feature points of the manipulator are extracted. The 3-D mapping is carried out on the acquired RGB image, and the depth and RGB values of feature points, as well as position and attitude information are calculated by using the Gauss mixture model. Finally, the target is extracted by combining the covariance matrix of feature points. The experimental results show that the co-ordinate error and angle error of the robot arm extracted by this method are small. The maximum extraction error is only 28%, which is much lower than the traditional method, which shows that the proposed method is more applicable.

Virtual scene dynamic interactive image segmentation technology based on OpenGL module

A virtual scene dynamic interactive image segmentation technology based on OpenGL sky module is proposed to improve the performance of image dynamic interactive recognition algorithm and the recognition effect of image segmentation. First, an automatic dynamic interactive image segmentation algorithm is proposed, a probability segmentation model of Gauss mixture fracture based on multi-scale crack enhancement filter is established, and expectation maximization algorithm is used to determine the parameters of GMM (Gaussian Mixture Model) and the underlying segmentation; second, the drawing function of OpenGL is used to draw the graph, and the drawing method needs to be calculated. That is to say, a mathematical model is established, which converts the graph into the calculation of vertex coordinates for drawing. It uses an extended cost function to guide dynamic interactive image segmentation using a set of cracks from the population to be segmented. Finally, the validity of the algorithm is verified by experimental simulation.