  Constructing colorized point clouds from mobile laser scanning and images is
a fundamental work in surveying and mapping. It is also an essential
prerequisite for building digital twins for smart cities. However, existing
public datasets are either in relatively small scales or lack accurate
geometrical and color ground truth. This paper documents a multisensorial
dataset named PolyU-BPCoMA which is distinctively positioned towards mobile
colorized mapping. The dataset incorporates resources of 3D LiDAR, spherical
imaging, GNSS and IMU on a backpack platform. Color checker boards are pasted
in each surveyed area as targets and ground truth data are collected by an
advanced terrestrial laser scanner (TLS). 3D geometrical and color information
can be recovered in the colorized point clouds produced by the backpack system
and the TLS, respectively. Accordingly, we provide an opportunity to benchmark
the mapping and colorization accuracy simultaneously for a mobile
multisensorial system. The dataset is approximately 800 GB in size covering
both indoor and outdoor environments. The dataset and development kits are
available at https://github.com/chenpengxin/PolyU-BPCoMa.git.
