Automatic expert system for 3D terrain reconstruction based on stereo vision and histogram matching

This paper proposes an automatic expert system for 3D terrain reconstruction and automatic intensity correction in stereo pairs of images based on histogram matching. Different applications in robotics, particularly those based on autonomous navigation in rough and natural environments, require a high-quality reconstruction of the surface. The stereo vision system is designed with a defined geometry and installed onboard a mobile robot, together with other sensors such as an Inertial Measurement Unit (IMU), necessary for sensor fusion. It is generally assumed the intensities of corresponding points in two images of a stereo pair are equal. However, this assumption is often false, even though they are acquired from a vision system composed of two identical cameras. We have also found this issue in our dataset. Because of the above undesired effects the stereo matching process is significantly affected, as many correspondence algorithms are very sensitive to these deviations in the brightness pattern, resulting in an inaccurate terrain reconstruction. The proposed expert system exploits the human knowledge which is mapped into three modules based on image processing techniques. The first one is intended for correcting intensities of the stereo pair coordinately, adjusting one as a function of the other. The second one is based in computing disparity, obtaining a set of correspondences. The last one computes a reconstruction of the terrain by reprojecting the computed points to 2D and applying a series of geometrical transformations. The performance of this method is verified favorably.

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