Gaussian mixture algorithm(GMA) is an effective approach for off-road terrain estimation,but still suffers from some difficulties in practical applications,such as complex calculation and object abstraction. In this paper,GMA is modified to improve its real-time performance and to provide it with a potential ability of obstacle detection. First,a selection window is designed based on the dominant-ellipse-principle to limit the probability distribution area of each measurement point,therefore avoiding the calculation on the cells outside the dominant ellipse. Second,a clustering approach is proposed in order to distinguish objects efficiently and decrease the operation area of one laser scan. Third,a virtual point vector is introduced to further reduce the computational load of the mean square error matrix. The modified GMA is experimented on a tracked mobile robot,and its improved performance is shown in comparison to the original GMA.
