In process of outdoor autonomous mobile robot visual navigation

shadows

causing cracks and irregular road boundary are encountered

which make the detection algorithms not so robust. The objectives of this paper are to resolve these problems. The proposed method in this paper is called a fast adaptive road detection method evolving adjustable gray thresholds per frame. First

a two-dimensional discrete wavelet analysis is used for road image decomposition and reconstruction. After comparing the approximate wavelet reconstruction of the road image in multiple-levels

a best resolution grade is determined which does not affect the "road-non-road" classification. In the best scale space

the grayscale maximum variance between-class and minimum variance within-class are used to create a fitness function

and the improved genetic algorithm is used in road image segmentation with each frame having an adaptive grayscale segmentation threshold. After that

the accurate road boundary is found

and if using the nearest two boundaries to calculate the central position of the road

the robot can know its driving directions. Content of main experiments: In this paper

a small autonomous land vehicle is used as a research platform

and the algorithm is tested by the outdoor path driving video of a mobile robot provided by CMU. The experimental results show that this method can detect the boundaries robustly under varying road conditions including shadows

cracks

and illumination changes. The real-time performance of the road detection system is good. The robot with this algorithm can run at a speed of 30 km/h on the school road covered with shadows

and the process rate of the vision system can reach to 20 ms per frame. Comparisons with reviewed researches: This segmentation method showed stronger self-adaptability to the environment than the traditional gray level histogram based segmentation method. A robust detection for the outdoor road is realized by the method of this paper. The proposed method in this paper can be seen as a robust method to the outdoor autonomous mobile robot's unstructured road detection

and should been extended.