Service Needs

Reference No.

N-0189

Title

LiDAR-Based Fiducial Marker Positioning System

Business Needs / Challenges

This project aims to develop an artificial intelligence (AI) technology, known as LiDAR-based fiducial marker positioning system, to provide accurate positioning in GPS-denied tunnel environment for tasks such as tunnel inspection in public works projects. The objective is to address the critical business needs for safer and more efficient tunnel inspection in the confined and hectic tunnel construction environment.

In the Trunk Road T2 and Cha Kwo Ling Tunnel project (the T2 project), “3S Tunnel Defect Inspector” – an AI drone system with onboard AI-based edge computing devices, which is the first AI drone system in Hong Kong for tunnel defect inspection, was deployed for efficient and automatic real-time defect inspection of TBM tunnel lining segments, viz concrete crack, concrete spalling and water leakage during the construction stage. This is a breakthrough to the conventional visual inspection in minimising safety risks of working at height, reducing tedious and repetitive tasks for human and enhancing tunnel construction logistics.

However, the “3S Tunnel Defect Inspector” requires a unique QR code on each tunnel segment and an established tunnel BIM model to determine the location of observed defects. The application of this AI inspection system is suitable for use in most new tunnel projects for which the two prerequisites would have been allowed for in the contract. However, this is limiting the broader application of the system.

Our project here is to bring a generalised solution for real-time positioning in tunnels for mobile robots that are assigned to carry out position-related tasks, by leveraging AI identification of fiducial marker, LiDAR 3-D point cloud models and an AI platform for evaluating and correcting the drift associated with the SLAM (Simultaneous Localization and Mapping) technique, such that the mobile robot can work out its own correcting maneuver to improve its position accuracy.

This real-time positioning method can be applied to determine both the locations of inspection robot and the defects observed in tunnel, with the benefits of enhanced safety, more efficient inspection process, minimal installation required, high adaptability to all tunnels and low maintenance cost.

(Note : If necessary, LiDAR scanning and construction of 3D Point Cloud Model can be carried out by the Trunk Road T2’s on site survey team to facilitate the PoC development. Equipment employed will be (1) Leica RTC360 (tripod-mounted laser scanner) and/or Leica BLK2GO (handheld laser scanner), using the supplied software to construct the 3D point cloud model.)

Application Areas

Infrastructure

Expected Outcomes

To complete the Proof-of-Concept (PoC) development within six months, and provide the required equipment and AI training data as necessary.

Technologies to be Used

Artificial Intelligence (AI)
Data Analytics
Deep Learning
Machine Learning
Computer Vision