PolyU-BPCoMa: A Dataset and Benchmark Towards Mobile Colorized Mapping Using a Backpack Multisensorial System

TL;DR

This paper introduces PolyU-BPCoMa, a comprehensive multisensor dataset for mobile colorized mapping, enabling benchmarking of geometric and color accuracy in indoor and outdoor environments.

Contribution

It provides a large-scale, multi-sensor dataset with ground truth for colorized point cloud mapping, addressing limitations of previous datasets.

Findings

Dataset covers 800 GB of indoor and outdoor environments.

Allows benchmarking of mapping and colorization accuracy.

Includes synchronized LiDAR, imaging, GNSS, and IMU data.

Abstract

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.