Abstract
This brief study introduces a partially working concept being developed at IT4Innovations supercomputer (HPC) facility. This concept consists of several modules that form a whole body of an efficient system for observation of terrain or objects displacements using satellite SAR interferometry (InSAR). A metadata database helps to locate data stored in various storages and to perform basic analyzes. A special database has been designed to describe Sentinel-1 data, on its burst level. Custom Sentinel-1 TOPS processing algorithms allow an injection of coregistered bursts into the database. Once the area of interest is set and basic processing parameters are given, the selected data are merged and processed by the Persistent Scatterers (PS) InSAR method or an optimized Small Baselines (SB) InSAR derivative. Depending on the expected deliverables, the processing results can be post-analyzed using a custom approach, in order to achieve a set of reliable measurement points. Final results can be post processed and visualized using a custom GIS toolbox, consisting in open-source GIS functionality. The GIS post-processing is enforced by HPC power as well. To demonstrate the practical applicability of the described system, a subsidence area in Konya city, Turkey is used as the study area for Sentinel-1 InSAR evaluation. (C) 2016 The Authors. Published by Elsevier B.V.

Abstract
Positioning and orientation precision of a multirotor aerial robot can be increased by using additional control loops for each of the driving units. As a result, one can eliminate lack of balance between true thrust forces. A control performance comparison of two proposed thrust controllers, namely robust controller designed with coefficient diagram method (CDM) and proportional, integral and derivative (PID) controller tuned with pole-placement law, is presented in the paper. The research has been conducted with respect to model/plant matching uncertainty and with the use of anti-windup compensators for a simple motor-rotor model approximated by first-order inertia plus delay. From the obtained simulation results one concludes that appropriate choice of AWC compensator improves tracking performance and increases robustness against parametric uncertainty.

Abstract
This paper evaluates different wavelet-based cross-spectral image fusion strategies adopted to merge visible and infrared images. The objective is to find the best setup independently of the evaluation metric used to measure the performance. Quantitative performance results are obtained with state of the art approaches together with adaptations proposed in the current work. The options evaluated in the current work result from the combination of different setups in the wavelet image decomposition stage together with different fusion strategies for the final merging stage that generates the resulting representation. Most of the approaches evaluate results according to the application for which they are intended for. Sometimes a human observer is selected to judge the quality of the obtained results. In the current work, quantitative values are considered in order to find correlations between setups and performance of obtained results; these correlations can be used to define a criteria for selecting the best fusion strategy for a given pair of cross-spectral images. The whole procedure is evaluated with a large set of correctly registered visible and infrared image pairs, including both Near InfraRed (NIR) and Long Wave InfraRed (LWIR).

Abstract
Object representation is one of the most challenging tasks in robotics because it must provide reliable information in real-time to enable the robot to physically interact with the objects in its environment. To ensure reliability, a global object descriptor must be computed based on a unique and repeatable object reference frame. Moreover, the descriptor should contain enough information enabling to recognize the same or similar objects seen from different perspectives. This paper presents a new object descriptor named Global Orthographic Object Descriptor (GOOD) designed to be robust, descriptive and efficient to compute and use. The performance of the proposed object descriptor is compared with the main state-of-the-art descriptors. Experimental results show that the overall classification performance obtained with GOOD is comparable to the best performances obtained with the state-ofthe-art descriptors. Concerning memory and computation time, GOOD clearly outperforms the other descriptors. Therefore, GOOD is especially suited for real-time applications.

Abstract
In this paper, we present a novel methodology to compute a 3D scene representation. The algorithm uses macro scale polygonal primitives to model the scene. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Results show that the approach is capable of producing accurate descriptions of the scene. In addition, the algorithm is very efficient when compared to other techniques.

Abstract
Object representation is one of the most challenging tasks in robotics because it must provide reliable information in real-time to enable the robot to physically interact with the objects in its environment. To ensure robustness, a global object descriptor must be computed based on a unique and repeatable object reference frame. Moreover, the descriptor should contain enough information enabling to recognize the same or similar objects seen from different perspectives. This paper presents a new object descriptor named Global Orthographic Object Descriptor (GOOD) designed to be robust, descriptive and efficient to compute and use. We propose a novel sign disambiguation method, for computing a unique reference frame from the eigenvectors obtained through Principal Component Analysis of the point cloud of the target object view captured by a 3D sensor. Three principal orthographic projections and their distribution matrices are computed by exploiting the object reference frame. The descriptor is finally obtained by concatenating the distribution matrices in a sequence determined by entropy and variance features of the projections. Experimental results show that the overall classification performance obtained with GOOD is comparable to the best performances obtained with the state-of-the-art descriptors. Concerning memory and computation time, GOOD clearly outperforms the other descriptors. Therefore, GOOD is especially suited for real-time applications. The estimated object's pose is precise enough for real-time object manipulation tasks.