For construction projects to be successful, building floors must be measured effectively and precisely. Traditional floor measurement techniques frequently take a long time, require a lot of labor, and are prone to mistakes, which increases expenses, delays, and rework. To solve these problems, the usage of Robot Operating System (ROS) and Light Detection and Ranging (LiDAR) technology for floor measurement in building construction has been suggested. The suggested approach is mounting a LiDAR sensor on a mobile robot and collecting data using ROS. A well-liked open-source software framework called ROS is used to create and implement robotics applications. It offers a standardised framework for data gathering, processing, and analysis, making data management effective and adaptable. Programmable movement is possible for the mobile robot with the associated LiDAR sensor. The robot is guided through the space to be measured, and the LiDAR sensor generates a dense and accurate 3D point cloud of the floor surface. The point cloud data is then processed using ROS to generate a high-resolution 3D model of the floor surface. The 3D model can be further analyzed to obtain measurements such as area, volume, and elevation.

The proposed method of using LiDAR with ROS for floor measurement has several advantages over traditional methods. LiDAR provides highly accurate and precise measurements of floors, reducing the risk of errors and rework. ROS enables efficient data processing and analysis, reducing the time and effort required for data processing. The mobile robot platform allows for flexible and efficient data collection, reducing the need for manual labor and minimizing disruptions to ongoing construction activities. The use of LiDAR with ROS for floor measurement can improve the efficiency and accuracy of floor measurement, thereby contributing to the overall efficiency and quality of building construction projects. It enables the generation of precise 3D models that can be used for planning and design purposes, as well as for monitoring construction progress and quality control. It can also help in reducing construction time, improving the accuracy of the project, and minimizing costs.

However, the proposed method has some limitations and challenges that need to be addressed. The cost of LiDAR sensors can be high, and specialized expertise in ROS programming may be required. Additionally, the presence of obstructions, such as furniture or equipment, can affect the accuracy of the LiDAR sensor's measurements.