Abstract. Indoor Mobile Mapping Systems (IMMS) are attracting growing attention, especially when LiDAR sensors are considered, thanks to the possibility to obtain wide range and complete data in those areas where GNSS signal is not available. However, the drift error that accumulates during the acquisition is often inadequate in the absence of quality constraints in case of extensive acquisitions. Concurrently, recent developments regarding multicamera mobile solutions have shown promising results in containing the drift error, but data produced are too noisy and not enough complete in terms of acquisition range. This paper compares a Laser Scanner IMMS and a multicamera system in a stress test concerning the survey of a complex and extended route. The two systems are the Laser Scanner Backpack IMMS Heron MS Twin Color produced by Gexcel and a laboratory prototype of a handheld photogrammetric multicamera named Ant3D. The objectives are to calculate and compare the drift errors and to evaluate the quality of the produced point clouds. Quantitative results demonstrate that the drift error per meter of trajectory for the Heron Backpack is 10 times greater than the one of the multicamera. From a qualitative aspect, Heron Backpack generates 3D data in a wider range, allowing a more complete reconstruction of the environment when compared to the multicamera system one. On the other hand, the encumbrance and manoeuvrability of Ant3D make it more versatile in surveying very narrow spaces.

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.