Abstract. Technological developments of the last decades are making possible to speed up different processes involved in construction projects. It is noticeable what building information modeling (BIM) can offer during the entire lifecycle of a project by integrating graphical and non graphical data, in addition to this, mapping the site with a 3D laser scan has been proved to provide a feasible workflow to compare as built models with as designed BIM, in this way, an automatic construction progress monitoring can also be performed. Terrestrial laser scanners (TLS) are commonly used to map a construction site due the level of accuracy provided, but indoor mobile mapping systems (iMMS) could offer a more efficient approach by speeding up the acquisition time and capturing all the details of the site just by walking through it, provided that the point cloud is accurate enough for the purpose of interest. In this paper, an iMMS is used to track the progress of a construction site, the point clouds were uploaded onto a platform of autonomous construction progress monitoring to verify if the system can meet the requirements of available applications. The results showed that the iMMS used is capable to produce point clouds with a quality such that the construction progress monitoring can be performed.

Abstract. Fixed-wing Unmanned Aerial Vehicles (UAV) and wearable or portable Mobile Mapping Systems (MMS) are two widely used platforms for point cloud acquisition with Light Detection And Ranging (LiDAR) sensors. The two platforms acquire from distant viewpoints and produce complementary point clouds, one describing predominantly horizontal surfaces and the other primarily vertical. Thus, the registration of the two data is not straightforward. This paper presents a test of targetless registration between a UAV LiDAR point cloud and terrestrial MMS surveys. The case study is a vegetated hilly landscape characterized by the presence of a structure of interest; the UAV acquisition allows the entire area to be acquired from above, while the terrestrial MMS acquisitions will enable the construction of interest to be detailed. The paper describes the survey phase with both techniques. It focuses on processing and registration strategies to fuse the two data together.Our approach is based on the ICP (Iterative Closest Point) method by exploiting the data processing algorithms available in the Heron Desktop post-processing software for handling data acquired with the Heron Backpack MMS instrument. Two co-registration methods are compared. Both ways use the UAV point cloud as a reference and derive the registration of the terrestrial MMS data by finding ICP matches between the ground acquisition and the reference cloud exploiting only a few areas of overlap. The two methods are detailed in the paper, and both allow us to complete the co-registration task.