Accurate localization and reliable mapping is essential for autonomous navigation of
robots. As one of the core technologies for autonomous navigation, Simultaneous Localization
and Mapping (SLAM) has attracted widespread attention in recent decades. Based on vision or
LiDAR sensors, great efforts have been devoted to achieving real-time SLAM that can support a
robot’s state estimation. However, most of the mature SLAM methods generally work under the
assumption that the environment is static, while in dynamic environments they will yield degenerate
performance or even fail. In this paper, first we quantitatively evaluate the performance of the
state-of-the-art LiDAR-based SLAMs taking into account different pattens of moving objects in the
environment. Through semi-physical simulation, we observed that the shape, size, and distribution
of moving objects all can impact the performance of SLAM significantly, and obtained instructive
investigation results by quantitative comparison between LOAM and LeGO-LOAM. Secondly, based
on the above investigation, a novel approach named EMO to eliminating the moving objects for
SLAM fusing LiDAR and mmW-radar is proposed, towards improving the accuracy and robustness
of state estimation. The method fully uses the advantages of different characteristics of two sensors
to realize the fusion of sensor information with two different resolutions. The moving objects can
be efficiently detected based on Doppler effect by radar, accurately segmented and localized by
LiDAR, then filtered out from the point clouds through data association and accurate synchronized
in time and space. Finally, the point clouds representing the static environment are used as the
input of SLAM. The proposed approach is evaluated through experiments using both semi-physical
simulation and real-world datasets. The results demonstrate the effectiveness of the method at
improving SLAM performance in accuracy (decrease by 30% at least in absolute position error) and
robustness in dynamic environments.