  This paper presents an efficient solution to 3D-LiDAR-based Monte Carlo
localization (MCL). MCL robustly works if particles are exactly sampled around
the ground truth. An inertial navigation system (INS) can be used for accurate
sampling, but many particles are still needed to be used for solving the 3D
localization problem even if INS is available. In particular, huge number of
particles are necessary if INS is not available and it makes infeasible to
perform 3D MCL in terms of the computational cost. Scan matching (SM), that is
optimization-based localization, efficiently works even though INS is not
available because SM can ignore movement constraints of a robot and/or device
in its optimization process. However, SM sometimes determines an infeasible
estimate against movement. We consider that MCL and SM have complemental
advantages and disadvantages and propose a fusion method of MCL and SM. Because
SM is considered as optimization of a measurement model in terms of the
probabilistic modeling, we perform measurement model optimization as SM. The
optimization result is then used to approximate the measurement model
distribution and the approximated distribution is used to sample particles. The
sampled particles are fused with MCL via importance sampling. As a result, the
advantages of MCL and SM can be simultaneously utilized while mitigating their
disadvantages. Experiments are conducted on the KITTI dataset and other two
open datasets. Results show that the presented method can be run on a single
CPU thread and accurately perform localization even if INS is not available.
