Real-time container position estimation method using stereo vision for container auto-landing system

This paper presents a new method for container auto-landing system using stereo vision. The position estimation of the spreader is very important for improving the operating efficiency of the port. A central problem in estimation of container position is that it is difficult to satisfy both the computation time problem and accuracy at the same time. To resolve this problem, we propose detection of container and estimation of distance from container to spreader using stereo vision. First, we extract region of container based on features in given a pair of stereo images. We detect lines of a container using Hough transform for extraction of morphological features. Then we extract candidate regions using crossing angle of straight lines. We segment the region of container using gray-labeling and perform experimental verification of geometric features. After that we match region of container based on area-based stereo matching approach. Through the process mentioned above, we get information of container position, a centroid, and a degree of slope. The performance of proposed method is verified on experimental results using container miniature which has size about 1/20 of the real one.