Reimaging the built environment with robotics and artificial intelligence.
RAIS4BE Lab at National University of Singapore pioneers robotic scanning and building information modelling (BIM) technology. Since the Lab started in 2021, we integrated LiDAR, image-based sensing, and semantic AI algorithms with mobile robotic platforms for automated 3D mapping, followed by high-precision BIM reconstruction and semantic augmentation of legacy buildings or infrastructures. We’re also interested in new computational modelling and design methods that couple BIM with AI for structural optimisation. Our lab brings together researchers from Civil, Mechanical, Geodesy, Electrical and Computer Engineering.
Shaobo
Research Associate (9.2024-)
Difeng
PhD (8.2021-12.2024)
Mike
Research Associate (3.2024-)
Vincent Gan
Assistant Professor (9.2020-)
Qiao
PhD (5.2022-5.2025) Began 2021 with Dr Wang
Tao
PhD (8.2021-10.2024)
Xiuqi
PhD (8.2022-present)
Kexin
PhD (1.2022-present)
Xiayi
Master Researcher (1.2023-1.2024)
Melanie
PhD (6.2023-) Began 2022 with Prof Teo
Yushuo
Master Researcher (1.2023-9.2024)
Hui Lin
Master Researcher (6.2023-1.2025)
Jey Chandar
Research Assistant (1.2025-present)
Yuanyuan
Research Assistant (7.2024-present)
Ruoming
Visiting PhD (10.2022-10.2023)
Josh
Research Assistant (1.2025-)
Xiang Chao
Visiting PhD (8.2022-8.2023)
Zhuang Dian
Visiting PhD (8.2021-8.2022)
We welcome prospective researchers to review our work and see if it aligns your long-term goals. Reach out if you’d like to know more.
Ben Ben
Lab mascot (1.2021-present)
Robotic 3D mapping for NUS
Our Research & Teaching
CDE Innovation Day Award
Teaching Excellent Award
Featured Publications
Hu, D.F., Gan, V.J.L.,* • Automation in Construction (2.2025)
Semantic navigation for automated robotic inspection and indoor environment quality monitoring
This paper proposes a semantic navigation approach to improve robotic inspection. A revised RandLA-Net and KNN algorithm construct a semantic map rich in detailed object information. An object instance reasoning algorithm identifies and extracts target object coordinates from the semantic map. A semantics-aware A* algorithm calculates safer, efficient navigation paths.
Gan, V.J.L., Hu, D.F.,* etc. Computer-Aided Civil and Infrastructure Engineering (3.2025)
Automated indoor 3D scene reconstruction with decoupled mapping using quadruped robot and LiDAR sensor
This study introduces an optimization algorithm incorporating viewpoint generation, occlusion detection and culling, and robot-moving trajectory identification. The research investigates 3D reconstruction, comparing coupled and decoupled approaches to identify most practical configuration for robotic scanning.
Gan, V.J.L., Li, K.X.,* etc. • Applied Energy (1.2025)
3D reconstruction of BIM with weakly-supervised learning for carbon emission modelling in the built environment
This paper presents an AI approach that employs weakly-supervised learning for automated BIM reconstruction, aiming at accurate carbon performance evaluation. By employing weakly-supervised semantic segmentation, this approach segments structural components from 3D point clouds and formulates the topological relationships of objects for BIM reconstruction. The BIM is used to assess upfront carbon footprint.
Zhai, R., Zou, J., Gan, V.J.L.,* etc. • Automation in Construction (10.2024)
Semantic enrichment of BIM with IndoorGML for quadruped robot navigation and automated 3D scanning
In this paper, BIM data schema is enriched with IndoorGML, integrating building geometry with spatial data to establish an indoor navigation model describing multi-scale spatial topological networks. This navigation model optimizes robot scanning positions and traversal sequences.
Wang, T., Gan, V.J.L.,* • Automation in Construction (10.2024)
Enhancing 3D reconstruction of textureless indoor scenes with IndoReal multi-view stereo
This paper presents the “IndoReal-MVS” dataset, a rich indoor-centric compilation reflecting real-world phenomena through advanced computer graphics. It introduces unsupervised “IndoorMatchNet”, synergising Feature Pyramid Network (FPN) and Pyramid Flowformer (PFF) for encoding complex indoor geometries.
Hu, D., Gan, V.J.L.,* etc. • Building and Environment (8.2022)
Multi-agent robotic system (MARS) for UAV-UGV path planning and automatic sensory data collection in cluttered environments
This paper presents a multi-agent robotic system for automatic UAV-UGV path planning and indoor navigation to automate sensory data collection. An enhanced shunting short-term memory model is proposed to optimise the pathfinding, 2D image and 3D point cloud data collection.
Gan, V.J.L.,* • Automation in Construction (2.2022)
BIM-based graph data model for automatic generative design of modular buildings
This paper presents a Building Information Modelling (BIM)-based graph data model for the theoretic representation of spatial attributes, topological relationships, geometries, and semantics for generative design of modular buildings.
PI initiated and led several research topics including algorithm development, coding, experiment and manuscript writing with lab members. If you’d like to know more, feel free to approach the PI.
Collaboration & Showcase
Forging New Frontiers - Robotics
(cde.nus.edu.sg/cde-research-jan2025)