Browsing by Author "Scannapieco, Antonio"

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    FPGA-based multi-sensor relative navigation in space: Preliminary analysis in the framework of the I3DS H2020 project
    (Internation Astronautical Federation, 2018-10-04) Estébanez Camarena, Monica; Feetham, Luke; Scannapieco, Antonio; Aouf, Nabil
    The Horizon 2020 Integrated 3D Sensors (I3DS) project brings together the following entities throughout Europe: THALES ALENIA SPACE - France / Italy / UK / Spain, SINTEF (Norway), TERMA (Denmark), COSINE (Netherlands), PIAP Space (Poland), HERTZ Systems (Poland), and Cranfield University (UK). I3DS is co-funded under the Horizon 2020 EU research and development program and is part of the Strategic Research Cluster on Space Robotics Technologies. The ambition of I3DS is to produce a standardised modular Inspector Sensor Suite (INSES) for autonomous orbital and planetary applications for future space missions. Orbital applications encompass activities such as on-orbit servicing and repair, space rendezvous and docking, collision avoidance and active debris removal (ADR). Simultaneous localisation and surface mapping (SLAM) for planetary exploration and general navigation in an unknown environment for scientific purposes can be considered in planetary applications. These envisaged space applications can be tackled by exploiting the flexibility, high performance and long product life of FPGAs. Conventional FPGAs are subject to Single Event Upsets (SEU) due to space radiation, causing their failure. Therefore, space-graded FPGAs, such as those developed by Xilinx, are targeted within the I3DS project. Currently, the main use of the FPGA within the development of this robust end-to-end multi-sensor suite is for navigation and data pre-processing. The aim of this paper is to assess the capabilities of FPGAs to carry out complex operations, such as running navigation algorithms for space applications. The motivation for the development of the on-board software architecture is as follows: raw data, acquired from the various sensors – including, among others, a High Resolution camera, a stereo camera and a LiDAR – is pre-processed to ensure the provision of robust and optimised inputs to 3D navigation algorithms. Noise reduction and conversion into suitable formats for the successful application of navigation algorithms are therefore the main aims of the data pre-processing. Some techniques adopted in this phase include outlier rejection and data dimensionality reduction for large point clouds, e.g. from LiDAR, and geometric and radiometric correction of the images from the cameras. The pre-processed data will then feed state-of-the-art relative navigation algorithms. Some of the proposed navigation algorithms include Generalised Iterative Closest Point (GICP) for dense 3D point clouds, relative positioning with fiducial markers, and visual odometry. The system environment for the preliminary operation is a test-bench setup formed by a standard desktop computer and a non-space-graded FPGA (Xilinx UltraZed-EG FPGA). The choice of FPGA was based on the similarity of this board to other space-graded ones also provided by Xilinx. Experimental tests on the algorithms are being performed in the framework of the validation campaign for the I3DS project. Preliminary results indicate that the data pre-processing can be efficiently carried out on the FPGA board.
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    A novel outlier removal method for two-dimensional radar odometry
    (Institution of Engineering and Technology, 2019-06-05) Scannapieco, Antonio
    Autonomous navigation of platforms in complex environments has a key role in many applications. However, the environmental conditions could negatively affect the performance of electro-optical sensors. Hence, the idea of using radar odometry has been recently developed. However, it suffers from the presence of outliers in the scene as its electro-optical counterparts. This work presents a method to classify radar echoes as inliers or outliers for two-dimensional radar odometry, based on their range rate and bearing angle. The range rate and bearing angle are in fact combined to give a classification value, different for each target. At each acquisition time, the median of all classification values is computed. Since classification values of stationary targets, i.e. the inliers, cluster around the median, while moving targets, i.e. the outliers, exhibit larger distance from the median, stationary targets and moving targets can be separated. This is also useful for Sense-and-Avoid purposes. The method has been tested in simulated scenario to show effectiveness in detecting outliers and in real case scenario to demonstrate significant improvement in reconstruction of trajectory of platform, keeping the final error around 10% of the travelled distance. Further improvement is envisaged by integrating the method in the target tracking strategy.
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    Space-oriented navigation solutions with integrated sensor-suite: the I3DS H2020 project
    (International Astronautical Federation (IAF), 2018-10-04) Scannapieco, Antonio; Feetham, Luke; Camarena, Monica; Aouf, Nabil
    In all orbital applications, such as on-orbit servicing and repair, rendezvous and docking, active debris removal (ADR), and planetary applications, such as exploration of unknown environments for scientific purposes by means of rovers, GPS-denied navigation aspects have a very large impact on the successful outcome of missions. Having a sensor suite, and hence several different sensors, also requires, at the same time, a suite of navigation algorithms able to deal with different kinds of inputs. Some of them, however, can be shared between multiple sensors, after thorough pre-processing of the raw data. Additionally, the same kind of sensor can require two different navigation algorithms depending on the scenario. The work described in this paper aims to present and critically discuss the approach to precise relative navigation solutions with a complete suite of sensors and their performance in different space-oriented application scenarios. Standalone navigation filters are examined. In the case of a high-resolution camera for an orbital scenario, the pose of a target, with respect to a chaser, can be thoroughly obtained with the aid of fiducial markers. Stereo camera-based navigation is also addressed with visual odometry. In the case of a stereo camera the problem of scale estimation during odometry is solved by means of triangulation. Since the outputs of the sensor-suite are also dense 3D point clouds, Iterative Closest Point and Histogram of Distances (HoD) with Kalman filter approaches are analyzed, paying attention to the provision of correct sensor characterization. The results for each filter are exhaustively examined, highlighting their strengths and the points where some improvements can be achieved.