  Global point clouds that correctly represent the static environment features
can facilitate accurate localization and robust path planning. However, dynamic
objects introduce undesired ghost tracks that are mixed up with the static
environment. Existing dynamic removal methods normally fail to balance the
performance in computational efficiency and accuracy. In response, we present
BeautyMap to efficiently remove the dynamic points while retaining static
features for high-fidelity global maps. Our approach utilizes a binary-encoded
matrix to efficiently extract the environment features. With a bit-wise
comparison between matrices of each frame and the corresponding map region, we
can extract potential dynamic regions. Then we use coarse to fine hierarchical
segmentation of the $z$-axis to handle terrain variations. The final static
restoration module accounts for the range-visibility of each single scan and
protects static points out of sight. Comparative experiments underscore
BeautyMap's superior performance in both accuracy and efficiency against other
dynamic points removal methods. The code is open-sourced at
https://github.com/MKJia/BeautyMap.
