Obstacle detection is the main components of cross-country intelligent vehicle guidance.Cross-country environments always have changeful illuminations and complicated terrains.The paper presents a new cross-country obstacle detection method based on binocular vision system.First

we calibrated the parameters of the vision system and studied the coordination transform at first to eliminate the influence of terrains.Second the original images were preprocessed by Gaussian filter and contrast-limited adaptive histogram equalization(CLAHE)method to weaken the effect of noise

light and contrast.Harris corners were located with sub-pixel accurate.Third to guarantee the overall system real-time performance

feature-based matching techniques were studied and fundamental matrix was calculated based on random sample consensus(RANSAC).Fourth continuity restrain was studied to eliminate pseudo matching pairs.Finally data interpolation was introduced to build elevation maps.Edge extraction and morphological processing were concerned to accomplish obstacle detection.Experimental results for different conditions are presented in support of the obstacle detection technology.