Abstract
With recent progress in lifelong map-learning as well as in information sharing networks, compact representation of a large-size landmark database has become crucial. In this paper, we propose a compact binary code (e.g. 32bit code) landmark representation by employing the semantic hashing technique from web-scale image retrieval. We show how well such a binary representation achieves compactness of a landmark database while maintaining efficiency of the localization system. In our contribution, we investigate the cost-performance, the semantic gap, the saliency evaluation using the presented techniques as well as challenge to further reduce the resources (#bits) per landmark. Experiments using a high-speed car-like robot show promising results.
