F. Fissore

A Low Cost UWB Based Solution for Direct Georeferencing UAV Photogrammetry

Thanks to their flexibility and availability at reduced costs, Unmanned Aerial Vehicles (UAVs) have been recently used on a wide range of applications and conditions. Among these, they can play an important role in monitoring critical events (e.g., disaster monitoring) when the presence of humans close to the scene shall be avoided for safety reasons, in precision farming and surveying. Despite the very large number of possible applications, their usage is mainly limited by the availability of the Global Navigation Satellite System (GNSS) in the considered environment: indeed, GNSS is of fundamental importance in order to reduce positioning error derived by the drift of (low-cost) Micro-Electro-Mechanical Systems (MEMS) internal sensors. In order to make the usage of UAVs possible even in critical environments (when GNSS is not available or not reliable, e.g., close to mountains or in city centers, close to high buildings), this paper considers the use of a low cost Ultra Wide-Band (UWB) system as the positioning method. Furthermore, assuming the use of a calibrated camera, UWB positioning is exploited to achieve metric reconstruction on a local coordinate system. Once the georeferenced position of at least three points (e.g., positions of three UWB devices) is known, then georeferencing can be obtained, as well. The proposed approach is validated on a specific case study, the reconstruction of the facade of a university building. Average error on 90 check points distributed over the building facade, obtained by georeferencing by means of the georeferenced positions of four UWB devices at fixed positions, is 0.29 m. For comparison, the average error obtained by using four ground control points is 0.18 m.

Toward the use of smartphones for mobile mapping

Abstract This paper considers the use of a low cost mobile device in order to develop a mobile mapping system (MMS), which exploits only sensors embedded in the device. The goal is to make this MMS usable and reliable even in difficult environments (e.g. emergency conditions, when also WiFi connection might not work). For this aim, a navigation system able to deal with the unavailability of the GNSS (e.g. indoors) is proposed first. Positioning is achieved by a pedestrian dead reckoning approach, i.e. a specific particle filter has been designed to enable good position estimations by a small number of particles (e.g. 100). This specific characteristic enables its real time use on the standard mobile devices. Then, 3D reconstruction of the scene can be achieved by processing multiple images acquired with the standard camera embedded in the device. As most of the vision-based 3D reconstruction systems are recently proposed in the literature, this work considers the use of structure from motion to estimate the geometrical structure of the scene. The detail level of the reconstructed scene is clearly related to the number of images processed by the reconstruction system. However, the execution of a 3D reconstruction algorithm on a mobile device imposes several restrictions due to the limited amount of available energy and computing power. This consideration motivates the search for new methods to obtain similar results with less computational cost. This paper proposes a novel method for feature matching, which allows increasing the number of correctly matched features between two images according to our simulations and can make the matching process more robust.

Towards Surveying with a Smartphone

Photogrammetry is one of the most used techniques for monitoring and surveying. It is widely used in several applications and in different working conditions. Accuracy of photogrammetry reconstruction methods may change depending on the working conditions (e.g. the number of acquired images, lighting conditions, baselines between images), and it is strictly related to the success of the solution of the Structure from Motion problem. Despite its widely spread use and the ever growing improvements to the reconstruction technique, photogrammetry still does not reach the same level of reliability of laser scanning surveying techniques: significant issues may occur in photogrammetric reconstructions when in presence of lighting problems or when the object of interest is not sufficiently textured. However, it relies on the use of much cheaper tools with respect to laser scanning techniques and surveying is usually much faster. This paper aims at showing the potential improvement that can be obtained by introducing information provided by the navigation system in the 3D reconstruction algorithm: the goal is that of making the solution algorithm of the Structure from Motion problem more reliable and accurate. As a side effect, faster reconstruction is typically achieved. The technique is validated on a building using images and navigation information got from a standard smartphone.

Implementation and assessment of two density-based outlier detection methods over large spatial point clouds

Several technologies provide datasets consisting of a large number of spatial points, commonly referred to as point-clouds. These point datasets provide spatial information regarding the phenomenon that is to be investigated, adding value through knowledge of forms and spatial relationships. Accurate methods for automatic outlier detection is a key step. In this note we use a completely open-source workflow to assess two outlier detection methods, statistical outlier removal (SOR) filter and local outlier factor (LOF) filter. The latter was implemented ex-novo for this work using the Point Cloud Library (PCL) environment. Source code is available in a GitHub repository for inclusion in PCL builds.Two very different spatial point datasets are used for accuracy assessment. One is obtained from dense image matching of a photogrammetric survey (SfM) and the other from floating car data (FCD) coming from a smart-city mobility framework providing a position every second of two public transportation bus tracks.Outliers were simulated in the SfM dataset, and manually detected and selected in the FCD dataset. Simulation in SfM was carried out in order to create a controlled set with two classes of outliers: clustered points (up to 30 points per cluster) and isolated points, in both cases at random distances from the other points. Optimal number of nearest neighbours (KNN) and optimal thresholds of SOR and LOF values were defined using area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Absolute differences from median values of LOF and SOR (defined as LOF2 and SOR2) were also tested as metrics for detecting outliers, and optimal thresholds defined through AUC of ROC curves.Results show a strong dependency on the point distribution in the dataset and in the local density fluctuations. In SfM dataset the LOF2 and SOR2 methods performed best, with an optimal KNN value of 60; LOF2 approach gave a slightly better result if considering clustered outliers (true positive rate: LOF2 = 59.7% SOR2 = 53%). For FCD, SOR with low KNN values performed better for one of the two bus tracks, and LOF with high KNN values for the other; these differences are due to very different local point density. We conclude that choice of outlier detection algorithm very much depends on characteristic of the dataset’s point distribution, no one-solution-fits-all. Conclusions provide some information of what characteristics of the datasets can help to choose the optimal method and KNN values.