I. Toschi

Investigation of indoor and outdoor performance of two portable mobile mapping systems

The paper investigates the performances of two portable mobile mapping systems (MMSs), the handheld GeoSLAM ZEB-REVO and Leica Pegasus:Backpack, in two typical user-case scenarios: an indoor two-floors building and an outdoor open city square. The indoor experiment is characterized by smooth and homogenous surfaces and reference measurements are acquired with a time-of-flight (ToF) phase-shift laser scanner. The noise of the two MMSs is estimated through the fitting of geometric primitives on simple constructive elements, such as horizontal and vertical planes and cylindrical columns. Length measurement errors on different distances measured on the acquired point clouds are also reported. The outdoor tests are compared against a MMSs mounted on a car and a robust statistical analysis, entailing the estimation of both standard Gaussian and non-parametric estimators, is presented to assess the accuracy potential of both portable systems.

Improving automated 3D reconstruction methods via vision metrology

This paper aims to provide a procedure for improving automated 3D reconstruction methods via vision metrology. The 3D reconstruction problem is generally addressed using two different approaches. On the one hand, vision metrology (VM) systems try to accurately derive 3D coordinates of few sparse object points for industrial measurement and inspection applications; on the other, recent dense image matching (DIM) algorithms are designed to produce dense point clouds for surface representations and analyses. This paper strives to demonstrate a step towards narrowing the gap between traditional VM and DIM approaches. Efforts are therefore intended to (i) test the metric performance of the automated photogrammetric 3D reconstruction procedure, (ii) enhance the accuracy of the final results and (iii) obtain statistical indicators of the quality achieved in the orientation step. VM tools are exploited to integrate their main functionalities (centroid measurement, photogrammetric network adjustment, precision assessment, etc.) into the pipeline of 3D dense reconstruction. Finally, geometric analyses and accuracy evaluations are performed on the raw output of the matching (i.e. the point clouds) by adopting a metrological approach. The latter is based on the use of known geometric shapes and quality parameters derived from VDI/VDE guidelines. Tests are carried out by imaging the calibrated Portable Metric Test Object, designed and built at University College London (UCL), UK. It allows assessment of the performance of the image orientation and matching procedures within a typical industrial scenario, characterised by poor texture and known 3D/2D shapes.

Sensor Data Integration for Landslide Monitoring—the LEMONADE Concept

The project LEMONADE (LandslidE MOnitoriNg And Data intEgration) aims to combine different techniques investigating their benefits and drawbacks. We present the different techniques used to monitor the active Corvara landslide located in the Italian Dolomites. Satellite remote sensing products allow covering the whole landslide providing 1D displacement measurements while proximal and terrestrial techniques can provide 3D information. In this paper, preliminary results considering each individual method applied are discussed and a first estimation of landslide displacements for the period considered is given.

Contextual classification using photometry and elevation data for damage detection after an earthquake event

ABSTRACT This research presents a processing workflow to automatically find damaged building areas in an urban context. The input data requirements are high-resolution multi-view images, acquired from airborne platform. The elevations are derived from a dense surface model generated with photogrammetric methods. With the principal objective of rapid response in emergency situations, two different processing roadmaps are proposed, semi-supervised and unsupervised. Both of them follow a two-step workflow of building detection and building health estimation. Optionally, cadastral layers may serve as a-priori knowledge on building location. The semi-supervised approach involves a data training step, while the unsupervised approach exploits the similarities and dissimilarities between sets of features calculated over the detected buildings. The change detection task is formulated as a classification task defined over a conditional random field. The algorithms are evaluated using two datasets (Vexcel and Midas cameras) and results are compared with ground truth data and specific metrics.