SLABIM: A SLAM-BIM Coupled Dataset in HKUST Main Building

TL;DR

SLABIM is the first dataset coupling SLAM and BIM data for a university building, enabling improved indoor mapping, localization, and semantic understanding with multi-sensor data and open-source availability.

Contribution

This work introduces SLABIM, a novel dataset combining SLAM and BIM data for indoor mapping, addressing a gap in existing datasets focused mainly on robot sensing.

Findings

Demonstrated effectiveness in registration, localization, and semantic mapping tasks.

Provided comprehensive, timestamped sensor data for building modeling.

Validated the dataset's practicality through experimental results.

Abstract

Existing indoor SLAM datasets primarily focus on robot sensing, often lacking building architectures. To address this gap, we design and construct the first dataset to couple the SLAM and BIM, named SLABIM. This dataset provides BIM and SLAM-oriented sensor data, both modeling a university building at HKUST. The as-designed BIM is decomposed and converted for ease of use. We employ a multi-sensor suite for multi-session data collection and mapping to obtain the as-built model. All the related data are timestamped and organized, enabling users to deploy and test effectively. Furthermore, we deploy advanced methods and report the experimental results on three tasks: registration, localization and semantic mapping, demonstrating the effectiveness and practicality of SLABIM. We make our dataset open-source at https://github.com/HKUST-Aerial-Robotics/SLABIM.