  In this paper, we explore the challenging 1-to-N map matching problem, which
exploits a compact description of map data, to improve the scalability of map
matching techniques used by various robot vision tasks. We propose a first
method explicitly aimed at fast succinct map matching, which consists only of
map-matching subtasks. These tasks include offline map matching attempts to
find a compact part-based scene model that effectively explains each map using
fewer larger parts. The tasks also include an online map matching attempt to
efficiently find correspondence between the part-based maps. Our part-based
scene modeling approach is unsupervised and uses common pattern discovery (CPD)
between the input and known reference maps. This enables a robot to learn a
compact map model without human intervention. We also present a practical
implementation that uses the state-of-the-art CPD technique of randomized
visual phrases (RVP) with a compact bounding box (BB) based part descriptor,
which consists of keypoint and descriptor BBs. The results of our challenging
map-matching experiments, which use a publicly available radish dataset, show
that the proposed approach achieves successful map matching with significant
speedup and a compact description of map data that is tens of times more
compact. Although this paper focuses on the standard 2D point-set map and the
BB-based part representation, we believe our approach is sufficiently general
to be applicable to a broad range of map formats, such as the 3D point cloud
map, as well as to general bounding volumes and other compact part
representations.
