In construction automation, robotic solution is becoming an emerging technology with the advent of artificial intelligence and advancement in mechatronic systems. In construction buildings, regular inspections are carried out to ensure project completion as per approved plans and quality standards. Currently, expert human inspectors are deployed onsite to perform inspection tasks with the naked eye and conventional tools. This process is time-consuming, labor-intensive, dangerous, and repetitive and may yield subjective results. In this article, we propose a robotic system equipped with perception sensors and intelligent algorithms to help construction supervisors remotely identify the construction materials, detect component installations and defects, and generate report of their status and location information. The building information model (BIM) is used for mobile robot navigation and to retrieve building component’s location information. Unlike the current deep learning-based object detection, which depends heavily on training data, this article proposes a data and information-driven approach, which incorporates offline training data, sensor data, and BIM information to achieve BIM-based object coverage navigation, BIM-based false detection filtering, and a fine maneuver technique to improve on object detections during real-time automated task execution by robots. This allows the user to select building components to be inspected, and the mobile robot navigates autonomously to the target components using the BIM-generated navigation map. An object detector then detects the building components and materials and generates an inspection report. The proposed system is verified through laboratory and onsite experiments.