3D reconstruction can be used as a platform to monitor the performance of activities on construction site, such as construction progress monitoring, structure inspection, and post-disaster rescue. Comparing to other sensors, RGB image has the advantages of low-cost, texture rich, and easy to implement that has been used as the primary method for 3D reconstruction in construction industry. However, the image-based 3D reconstruction always requires extended time to acquire and/or to process the image data, which limits its application on time critical projects. Recent progress in visual simultaneous localization and mapping (SLAM) make it possible to reconstruct a 3D map of construction site in real-time. Integrated with unmanned aerial vehicle (UAV), the obstacles areas that are inaccessible for the ground equipment can also be sensed. Despite these advantages of visual SLAM and UAV, until now, such technique has not been fully investigated on construction site. Therefore, the objective of this research is to present a pilot study of using visual SLAM and UAV for real-time construction site reconstruction. The system architecture and the experimental setup are introduced, and the preliminary results and the potential applications using visual SLAM and UAV on construction site are discussed.

This paper introduces a new UAV path planning method for creating high-quality 3D reconstruction models of large and complex structures. The core of the new method is incorporating the topology information of the surveyed 3D structure to decompose the multi-view stereo path planning into a collection of overlapped view optimization problems that can be processed in parallel. Different from the existing state-of-the-arts that recursively select the vantage camera views, the new method iteratively resamples all nearby cameras (i.e., positions/orientations) together and achieves a substantial reduction in computation cost while improving reconstruction quality. The new approach also provides a higher-level automation function that facilitates field implementations by eliminating the need for redundant camera initialization as in existing studies. Validations are provided by measuring the variance between the reconstructions to the ground truth models. Results from three synthetic case studies and one real-world application are presented to demonstrate the improved performance. The new method is expected to be instrumental in expanding the adoption of UAV-based multi-view stereo 3D reconstruction of large and complex structures.