  As technology advances in autonomous mobile robots, mobile service robots
have been actively used more and more for various purposes. Especially, serving
robots have been not surprising products anymore since the COVID-19 pandemic.
One of the practical problems in operating a serving robot is that it often
fails to estimate its pose on a map that it moves around. Whenever the failure
happens, servers should bring the serving robot to its initial location and
reboot it manually. In this paper, we focus on end-to-end relocalization of
serving robots to address the problem. It is to predict robot pose directly
from only the onboard sensor data using neural networks. In particular, we
propose a deep neural network architecture for the relocalization based on
camera-2D LiDAR sensor fusion. We call the proposed method FusionLoc. In the
proposed method, the multi-head self-attention complements different types of
information captured by the two sensors to regress the robot pose. Our
experiments on a dataset collected by a commercial serving robot demonstrate
that FusionLoc can provide better performances than previous end-to-end
relocalization methods taking only a single image or a 2D LiDAR point cloud as
well as a straightforward fusion method concatenating their features.
