  In the classical context of robotic mapping and localization, map matching is
typically defined as the task of finding a rigid transformation (i.e., 3DOF
rotation/translation on the 2D moving plane) that aligns the query and
reference maps built by mobile robots. This definition is valid in loop-rich
trajectories that enable a mapper robot to close many loops, for which precise
maps can be assumed. The same cannot be said about the newly emerging
autonomous navigation and driving systems, which typically operate in loop-less
trajectories that have no large loop (e.g., straight paths). In this paper, we
propose a solution that overcomes this limitation by merging the two maps. Our
study is motivated by the observation that even when there is no large loop in
either the query or reference map, many loops can often be obtained in the
merged map. We add two new aspects to map matching: (1) image retrieval with
discriminative deep convolutional neural network (DCNN) features, which
efficiently generates a small number of good initial alignment hypotheses; and
(2) map merge, which jointly deforms the two maps to minimize differences in
shape between them. To realize practical computation time, we also present a
preemption scheme that avoids excessive evaluation of useless map-matching
hypotheses. To verify our approach experimentally, we created a novel
collection of uncertain loop-less maps by utilizing the recently published
North Campus Long-Term (NCLT) dataset and its ground-truth GPS data. The
results obtained using these map collections confirm that our approach improves
on previous map-matching approaches.
