Accurate and Cost-Scalable Panorama Visual-Geometric-Matching Based Localization System for Robot Navigation

To operate a large number of autonomous robots, an accurate and low-cost localization technology is recom- mended. To meet this demand, we have proposed an original lo- calization approach, Visual-Geometric-Matching (VGM), using a monocular RGB camera and a pre-build map, and have de- veloped it as the client-server localization system. In this paper, we propose a panorama-VGM method, which achieves efficient memory usage and efficient image matching. By applying a cylindrical panorama transform to the template images, the redundant information is eliminated, and the VRAM memory usage is significantly reduced. The panorama transform is also applied to query image from a client, and both images are matched on panoramic projection surface. Furthermore, we integrated the results of panorama-VGM with the odometry data by Extended Kalman Filter and utilize for a mobile robot navigation. Experimental results show that the proposed panorama-VGM is accurate, robust and practical localization system. Specifically: (1) the median error is 0.11 [m] in the single algorithm accuracy evaluation, (2) the median error is 0.12 [m] in the mobile robot navigation experiment compared with LiDAR + EMCL, (3) the travel distance is 2,590 [m] during a continuous 77-minute durability test.