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.

翻译：本文提出了一种基于三维激光雷达的蒙特卡洛定位（MCL）高效解算方法。当粒子恰好采样于真实位姿附近时，MCL能够鲁棒工作。惯性导航系统（INS）可用于精确采样，但即使采用INS，求解三维定位问题仍需大量粒子。特别地，若无法使用INS，则需要海量粒子，这使得三维MCL因计算代价过高而难以实现。扫描匹配（SM）作为基于优化的定位方法，其优化过程可忽略机器人/设备的运动约束，因此即使无INS也能高效运行。然而，SM有时会得出违背运动规律的不可行估计。本文认为MCL与SM具有互补的优缺点，提出一种MCL与SM的融合方法。由于SM在概率建模框架下可视为量测模型优化，我们将其作为量测模型优化过程，利用优化结果近似量测模型分布，并基于该近似分布进行粒子采样。通过重要性采样将采样粒子与MCL融合，从而在抑制各自缺点的同时，同时发挥MCL与SM的优势。在KITTI数据集及另外两个公开数据集上的实验表明，所提方法可在单CPU线程上运行，且即使无INS也能实现准确定位。