首个融合SLAM与BIM的港科大建筑数据集，支持精准定位与语义建图。
SLABIM: A SLAM-BIM Coupled Dataset in HKUST Main Building
- 构建港科大主楼的SLAM与建筑信息模型（BIM）耦合数据集
- 多传感器采集多时段数据，实现设计与实际建筑模型匹配
- 适用于机器人定位、注册与语义映射研究，已开源
现有室内SLAM数据集多聚焦机器人感知，缺乏建筑结构信息。为填补此空白，我们设计并构建首个融合SLAM与建筑信息模型（BIM）的数据集——SLABIM。该数据集包含港科大主楼的设计与实际建筑模型，提供面向SLAM的多传感器数据。设计的BIM经分解与转换，便于使用。采用多传感器套件进行多时段数据采集与建图，获得实际建筑模型。所有数据均带时间戳并有序组织，支持高效部署与测试。此外，我们部署先进方法，在三个任务（配准、定位、语义映射）上报告实验结果，验证了SLABIM的有效性与实用性。数据集已在GitHub开源：https://github.com/HKUST-Aerial-Robotics/SLABIM。
原文摘要 · Abstract (English)
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.
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