Advanced LIDAR-based techniques for autonomous navigation of spaceborne and airborne platforms

The main goal of this PhD thesis is the development and performance assessment of innovative techniques for the autonomous navigation of aerospace platforms by exploiting data acquired by electro-optical sensors. Specifically, the attention is focused on active LIDAR systems since they globally provide a higher degree of autonomy with respect to passive sensors. Two different areas of research are addressed, namely the autonomous relative navigation of multi-satellite systems and the autonomous navigation of Unmanned Aerial Vehicles. The global aim is to provide solutions able to improve estimation accuracy, computational load, and overall robustness and reliability with respect to the techniques available in the literature. In the space field, missions like on-orbit servicing and active debris removal require a chaser satellite to perform autonomous orbital maneuvers in close-proximity of an uncooperative space target. In this context, a complete pose determination architecture is here proposed, which relies exclusively on three-dimensional measurements (point clouds) provided by a LIDAR system as well as on the knowledge of the target geometry. Customized solutions are envisaged at each step of the pose determination process (acquisition, tracking, refinement) to ensure adequate accuracy level while simultaneously limiting the computational load with respect to other approaches available in the literature. Specific strategies are also foreseen to ensure process robustness by autonomously detecting algorithms' failures. Performance analysis is realized by means of a simulation environment which is conceived to realistically reproduce LIDAR operation, target geometry, and multi-satellite relative dynamics in close-proximity. An innovative method to design trajectories for target monitoring, which are reliable for on-orbit servicing and active debris removal applications since they satisfy both safety and observation requirements, is also presented. On the other hand, the problem of localization and mapping of Unmanned Aerial Vehicles is also tackled since it is of utmost importance to provide autonomous safe navigation capabilities in mission scenarios which foresee flights in complex environments, such as GPS denied or challenging. Specifically, original solutions are proposed for the localization and mapping steps based on the integration of LIDAR and inertial data. Also in this case, particular attention is focused on computational load and robustness issues. Algorithms' performance is evaluated through off-line simulations carried out on the basis of experimental data gathered by means of a purposely conceived setup within an indoor test scenario

Uncooperative Spacecraft Relative Navigation With LIDAR-Based Unscented Kalman Filter

Autonomous relative navigation is a critical functionality which needs to be developed to enable safe maneuvers of a servicing spacecraft (chaser) in close-proximity with respect to an uncooperative space target, in the frame of future On-Orbit Servicing or Active Debris Removal missions. Due to the uncooperative nature of the target, in these scenarios, relative navigation is carried out exploiting active or passive Electro-Optical sensors mounted on board the chaser. The focus here is placed on active systems, e.g., LIDARs. In this paper, an original loosely-coupled relative navigation architecture which integrates pose determination algorithms designed to process raw LIDAR data (i.e., 3D point clouds) within a Kalman filtering scheme is presented. Pose determination algorithms play a twofold role being used to initialize the filter state and covariance as well as in the update phase of the Kalman filter. The proposed filtering scheme is an Unscented Kalman Filter designed to use, as measurements for the update phase, relative position, attitude and angular velocity estimates. Performance assessment is carried out within a simulation environment realistically reproducing the operation of a scanning LIDAR and the relative motion between two spacecraft during a target monitoring maneuver. The numerical simulation campaign demonstrates robustness of the proposed approach even when dealing with challenging conditions (e.g., low range measurement accuracy, low update rate and high point-cloud sparseness) determined by the LIDAR noise level and operational parameters

PCA-based line detection from range data for mapping and localization-aiding of UAVs

This paper presents an original technique for robust detection of line features from range data, which is also the core element of an algorithm conceived for mapping 2D environments. A new approach is also discussed to improve the accuracy of position and attitude estimates of the localization by feeding back angular information extracted from the detected edges in the updating map. The innovative aspects of the line detection algorithm regard the proposed hierarchical clusterization method for segmentation. Instead, line fitting is carried out by exploiting the Principal Component Analysis, unlike traditional techniques relying on Least Squares linear regression. Numerical simulations are purposely conceived to compare these approaches for line fitting. Results demonstrate the applicability of the proposed technique as it provides comparable performance in terms of computational load and accuracy compared to the least squares method. Also, performance of the overall line detection architecture, as well as of the solutions proposed for line-based mapping and localization-aiding is evaluated exploiting real range data acquired in indoor environments using an UTM-30LX-EW 2D LIDAR. This paper lies in the framework of autonomous navigation of unmanned vehicles moving in complex 2D areas, e.g. unexplored, full of obstacles, GPS-challenging or denied

A Highly Integrated Navigation Unit for On-Orbit Servicing Missions

VINAG (VISION/INS integrated Navigation Assisted by GNSS) is a highly integrated multisensor navigation unit, particularly conceived for On-Orbit Servicing missions. The system is designed to provide all-in-one, on-board real time autonomous absolute navigation as well as pose determination of an uncooperative known object orbiting in LEO (Low Earth Orbit), GEO (GEosynchronous Orbits) and possibly in HEO (Highly Earth Orbit). The system VINAG is under development by a team of Italian companies and universities, co-financed by the Italian Space Agency. Thanks to a tight optimized integration of its subsystems, VINAG is characterized by a low power and mass total budgets and therefore it is suitable for small and very small satellites. In order to provide both 1) absolute orbit and attitude determination and 2) vision-based pose determination, the unit integrates three metrology systems: a Cameras Subsystem (a monocular camera and a Star sensor), an Inertial Measurement Unit (IMU) and a GNSS (Global Navigation Satellite System) receiver. In this paper, we introduce the complete system architecture, the adopted algorithms and then the adopted hardware design solutions. In addition, we describe preliminary numerical simulation results obtained for different orbits from LEO to GEO carried out for the validation phase of VINAG

Experiência da Rede Paulista de ATS na parceria com a CONITEC

A constituição de redes de pesquisa para realização de estudos estratégicos, a exemplo de redes internacionais, permite aos atores assistenciais potencializar a produção de conhecimentos ou testes de conhecimentos sugeridos ou requeridos para o planejamento do sistema de saúde. A constituição inicial da Rede Paulista de Avaliação de Tecnologias em Saúde - REPATS - consiste em formalizar Grupos de Trabalho Temáticos visando incrementar qualidade assistencial e a produção de evidências, além dos benefícios em práticas assistenciais com base em evidências, melhor segurança do paciente, pode reduzir variação e custos injustificados. A eficiência na prestação dos serviços e otimização do uso dos escassos recursos do setor saúde pode aumentar o acesso e resolutividade ao sistema. Mediante demandas da CONITEC e necessidade de regularização das práticas assistenciais do SUS no Estado de São Paulo, os Comitês específicos de Farmacologia e Qualificação de Materiais dos Núcleos de Avaliação de Tecnologias em Saúde - NATS - da REPATS vêm produzindo conhecimentos estruturados de acordo com as Diretrizes do Ministério da Saúde, publicações científicas e algumas pós-graduações. A parceria com a CONITEC impulsionou o desenvolvimento da REPATS, criando fluxo e estímulo para a superação da cultura oral, auxiliando inclusive financeiramente alguns dos NATS

A Vision-Based Approach to UAV Detection and Tracking in Cooperative Applications

This paper presents a visual-based approach that allows an Unmanned Aerial Vehicle (UAV) to detect and track a cooperative flying vehicle autonomously using a monocular camera. The algorithms are based on template matching and morphological filtering, thus being able to operate within a wide range of relative distances (i.e., from a few meters up to several tens of meters), while ensuring robustness against variations of illumination conditions, target scale and background. Furthermore, the image processing chain takes full advantage of navigation hints (i.e., relative positioning and own-ship attitude estimates) to improve the computational efficiency and optimize the trade-off between correct detections, false alarms and missed detections. Clearly, the required exchange of information is enabled by the cooperative nature of the formation through a reliable inter-vehicle data-link. Performance assessment is carried out by exploiting flight data collected during an ad hoc experimental campaign. The proposed approach is a key building block of cooperative architectures designed to improve UAV navigation performance either under nominal GNSS coverage or in GNSS-challenging environments

Magnetometer Calibration for Small Unmanned Aerial Vehicles Using Cooperative Flight Data

This paper presents a new method to improve the accuracy in the heading angle estimate provided by low-cost magnetometers on board of small Unmanned Aerial Vehicles (UAVs). This task can be achieved by estimating the systematic error produced by the magnetic fields generated by onboard electric equipment. To this aim, calibration data must be collected in flight when, for instance, the level of thrust provided by the electric engines (and, consequently, the associated magnetic disturbance) is the same as the one occurring during nominal flight operations. The UAV whose magnetometers need to be calibrated (chief) must be able to detect and track a cooperative vehicle (deputy) using a visual camera, while flying under nominal GNSS coverage to enable relative positioning. The magnetic biases’ determination problem can be formulated as a system of non-linear equations by exploiting the acquired visual and GNSS data. The calibration can be carried out either off-line, using the data collected in flight (as done in this paper), or directly on board, i.e., in real time. Clearly, in the latter case, the two UAVs should rely on a communication link to exchange navigation data. Performance assessment is carried out by conducting multiple experimental flight tests