Estimating the observer position in 3D environment using a visual image data is called the egomotion. This method consists of three steps: in the first step, feature points of image are detected; in the second step, optical flow information is obtained; in the third and final step appropriate transformation parameters are calculated using the optimization method. Especially in enclosed areas where GPS can not be used or high estimation errors occured, in underground systems, visual egomotion is critically important. As well as the precise location estimation, providing a low cost solution is one of the superior properties of egomotion method. In this study, different egomotion methods has been observed, the numerical results in noisy and noiseless environment obtained by comparing the accuracy of algorithms presented in the table.