The Relocalization of SLAM Tracking Based on Spherical Cameras

Abstract

This work proposes a novel solution to relocalize the SLAM tracking based on spherical cameras. It focuses on the imaging method of spherical camera, the feature extracting algorithm and the relocalization of SLAM tracking based on the 3D reconstruction. In the imaging method, we design a new camera containing eight fish-eye lenses, and then we propose a calibration method to calibrate the eight fish-eye lenses spherical camera; To get the high-performance feature points of panoramic image, we propose a network based on a separate network to extract local feature accurately and quickly. With the correct key points obtained by the feature extracting method, we reoptimize the SLAM tracking after the maximum posteriori estimation usually applied in common back-end SLAM to relocalize the SLAM tracking. The experiment results show that the calibration method achieved 0.973 reprojection error, lower than the common methods like Zhang's or DLT. The inlier rate and matching time of proposed SimpGeoDesc are all better than the reference models ContextDesc and GeoDesc. With the correct feature points, SLAM tracking is clearer and more steadily with our relocalization method. That is the solution of relocalization of SLAM tracking proposed in this work is effective. The AR application of the relocalization proves the feasibility of our propose relocalization method.