A precise self-localization algorithm based on improved Harris feature point is proposed.It provides more reliable input for motion estimation through improving the strategies of feature detecting

matching and tracking

which improves the accuracy of motion estimation.The improved strategy of extracting Harris corners in Gaussian scale spaces is presented.The method not only makes the features be rotation and scale invariant

but also controls the quantity of the uniform distributed features.Using the IMU prior data to estimate the areas of the tracked features

which ensures the accuracy of feature-tracking and reduces the computing time.Quaternion and least square method are used to estimate the attitude information of the vehicle.Results of the experiments show that the algorithm fuses IMU data and visual information excellently

makes feature matching and tracking stably

avoids the drift problem of IMU data and achieves a higher localization precision.