Abstract
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 ismodified 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.
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Supported by the National Natural Science Foundation of China (Grant Nos. 60775056, 60705028)
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Qiu, Q., Yang, T. & Han, J. A new real-time algorithm for off-road terrain estimation using laser data. Sci. China Ser. F-Inf. Sci. 52, 1658–1667 (2009). https://doi.org/10.1007/s11432-009-0157-y
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DOI: https://doi.org/10.1007/s11432-009-0157-y