Automatical Acquisition of Point Clouds of Construction Sites and Its Application in Autonomous Interior Finishing Robot With the rapid increase of labor costs, it is urgent to use robots to replace part of the manpower in the construction industry. To achieve highly automated interior finishing operations, robots need to be able to sense the sites to be constructed. This paper proposes a solution that allows the robot to autonomously detect the environment to be constructed. And it can also achieve quality inspection and contribute to subsequent planning. The program uses the BIM model of the building as an input and uses the FARO laser scanner as the sensor. The system can automatically select the scan station of the scanner and sequentially move to the selected scan stations to perform scanning, thereby collecting highaccuracy point clouds data at the construction site. The application of the acquired point clouds is also introduced, including the construction quality inspection and the acquisition of house models with accurate dimensional parameters.

RobotBased Machining of Unmodeled Objects via Feature Detection in Dense Point Clouds Machining applications using robots are still not common in industrial settings. Reasons are the unintuitive programming concepts which typically require expert knowledge and the inflexibility regarding small alterations of the workpieces. We present a prototypical solution for an intuitive and flexible robotic machining concept for unmodeled work pieces. For this we use a high resolution laser scanner to record very dense point clouds. Algorithms to detect linear edges with obtuse angled corners, linear edges with acute angled corners, linear inner edges and circular edges were developed, demonstrated and validated. To accurately execute generated trajectories in practice, an algorithm to directly calibrate the transformation between the sensor and the milling tool was developed. For the algorithms and the calibration process a repeatability tolerance of 0.2 mm is achieved.