Abstract: According to the simulaneous localization and mapping(SLAM), a fast pose estimation of on-board camera, together with 3D reconstruction of scene structure algorithm was proposed. The special properties of the Euclidean epipoles corresponding to the mobile robots 2-DOF movement were utilized to calibrate the relative pose information between real camera coordinate and virtual camera coordinate without the utilization of specific calibration object, and the active vision method was utilized to further calibrate the relative position information between mobile robot coordinate and virtual camera coordinate; An constructed infinite homography was adopted to turn the hypothesis Euclidean point correspondences obtained by SIFT algorithm into the virtual hypothesis Euclidean point correspondences, and the RANSAC based normalized 3-point algorithm was implemented to estimate and decompose the essential matrix; The previous 3D information of the observed landmarks were adopted to eliminate the scale uncertainty of the translation vector acquired by essential matrix decomposition, and the scene structure was reconstructed by linear triangulation method. Experimental results show that the proposed algorithm has the advantages of high precision, as well as the low computational complexity.

Key words: essential matrix decomposition, random sample consensus, epipolar geometry, vision SLAM, normalized 3-point algorithm