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In this paper, we study how to build a vision-based system for global localization with accuracies within 10cm. for robots and humans operating both indoors and outdoors over wide areas covering many square kilometers. In particular, we study the parameters of building a landmark database rapidly and utilizing that database online for real-time accurate global localization. Although the accuracy of traditional short-term motion based visual odometry systems has improved significantly in recent years, these systems alone cannot solve the drift problem over large areas. Landmark based localization combined with visual odometry is a viable solution to the large scale localization problem. However, a systematic study of the specification and use of such a landmark database has not been undertaken. We propose techniques to build and optimize a landmark database systematically and efficiently using visual odometry. First, topology inference is utilized to find overlapping images in the database. Second, bundle adjustment is used to refine the accuracy of each 3D landmark. Finally, the database is optimized to balance the size of the database with achievable accuracy. Once the landmark database is obtained, a new real-time global localization methodology that works both indoors and outdoors is proposed. We present results of our study on both synthetic and real datasets that help us determine critical design parameters for the landmark database and the achievable accuracies of our proposed system.
Zhu et al. (Sun,) studied this question.