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.

Autonomous UAV 3D reconstruction has been widely used for infrastructure inspections and asset management. However, its applications on truss structures remain a challenging task due to geometric complexity and the severe self-occlusion problem of the truss structures when constrained by camera FOV, safety clearance, and flight duration. This paper proposes a new flight planning method to effectively address the self-occlusion problem to enable autonomous and efficient data acquisition for survey-grade 3D truss reconstruction. The proposed method contains two steps: First, identifying a minimal set of viewpoints achieves the maximal reconstruction quality at every observed surface of the truss geometry through an iterative optimization schema. Second, converting the optimal viewpoints into the shortest, collision-free flight trajectories while considering the UAV constraints. The computed flight path can also be implemented in a multi-UAV fashion. Evaluations of the proposed method include a synthetic truss bridge and a real-world truss bridge. The evaluation results suggested that the proposed approach outperforms the existing works in terms of 3D reconstruction quality while taking less time in both the inflight image acquisition and the post-flight 3D reconstruction.

UAVs showed great efficiency on scanning bridge decks surface by taking a single shot or through stitching a couple of overlaid still images. If potential surface deficits are identified through aerial images, subsequent ground inspections can be scheduled. This two-phase inspection procedure showed great potentials on increasing field inspection productivity. Since aerial and ground inspection images are taken at different scales, a tool to properly fuse these multi-scale images is needed for improving the current bridge deck condition monitoring practice. In response to this need a data fusion platform is introduced in this study. Using this proposed platform multi-scale images taken by different inspection devices can be fused through geo-referencing. As part of the platform, a web-based user interface is developed to organize and visualize those images with inspection notes under users queries. For illustration purpose, a case study involving multi-scale optical and infrared images from UAV and ground inspector, and its implementation using the proposed platform is presented.