1,721,101 research outputs found
Crater-based Autonomous Position Estimation in Planetary Missions by Deep-Learning
No full textSpacecraft missions venturing beyond Earth rely upon ranging, specific payloads or supports systems, claiming the usage of facilities such as Deep Space Network or ESTRACK. This requires a significant amount of resources both on Earth and onboard, especially in monetary terms. Furthermore, satellite link is not always guaranteed, and results are not available in real-time. Therefore, to cruise independently of Earth-based operators and to achieve the requirements raised by the next planetary exploratory missions, this manuscript proposes a novel visual-based terrain relative navigation (TRN) system. TRN is promising because can be applied to a wide range of space missions, e.g. planetary exploration (rocky planets), the study of moons of gaseous planets, approach phase of comets, asteroid, and other celestial bodies. In essence, a spacecraft can retrieve its absolute position by matching a pattern of observed craters with a database. The measurements thus obtained can be integrated into a navigation filter to estimate the spacecraft state (position and velocity). The ability to detect match surface features to a map is crucial for TRN. However, craters largely vary their appearances also depending on image qualities, lighting geometry, and noises. For these reasons, realizing a crater detector able to generalize to different scenarios is complex. It is worth considering that this task must be performed in a robust way to keep high the navigation accuracy. In past, this has led to least square approaches, creating situations where corrupted navigation states render otherwise good images ineffectual, leading to unnecessary filter reinitializations, trajectory aborts (e.g. during lunar descent), or other undesirable events. Contrarily, the solution proposed is reliable, combining the strengths of a region-based convolutional neural network (Mask R-CNN) with the robustness of projective invariants theory. An extended Kalman filter completes the TRN system, further increasing the stability of the system. Despite the usage of medium resolution (118 m/px) data, results showed that the navigation accuracy lies below 400 meters in the best-case scenario for a satellite orbiting around the Moon at about 50 km altitude. This is expected to guarantee real-time autonomous onboard operations with no need for ground support
Current methods for meteorological and marine forecasting for the assistance of navigation and shipping operations
No full textA Deep Learning-based Crater Detector for Autonomous Vision-Based Spacecraft Navigation
No full textThe paper proposes the use of Cascade Mask R-CNN for the detection of craters from monocular images. Crater detection is a challenging task being the images prone to changes in lighting and noise conditions. Besides, the crater appearance is strongly modified according to the region of interest, being the shadows strongly affected by the sun vector inclination. To tackle these issues, the paper exploits the generalizability of modern deep learning architectures to create a highly reliable crater detector. The dataset used for transfer learning the model comprises more than 800 real lunar monocular images obtained from the lunar reconnaissance orbiter (LRO) cameras. Results confirm the performance reached by the multi-stage object detection architecture both in equatorial and polar regions, its robustness, and the validity of this crater detection scheme for planetary navigation tasks
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Il ruolo attuale del microbiologo clinico nella gestione delle infezioni respiratorie (The role of the clinical microbiologist in the management of the respiratory infections today)
No full textRespiratory infections are widespread and are responsible for severe human infectious diseases. Their importance is related to the high economic and social costs due to health expenses for therapies and hospitalization, as well as lack of productivity Microbiological laboratory is still now a special concern, because of the possibility of a short time diagnosis, thanks also to molecular methods. Indeed, these methods offer the high advantage of the rapid identification of the microorganisms responsible for the infections, by overcoming the usually long times of the "in vitro" growth or making possible early administration of specific antibiotics and decreasing the risk for resistant strain selection. Clinical microbiologist appears, therefore, to be strategic for both diagnosis and therapeutic management of these patients
Cascade Mask R-CNN Architecture for crater detection in autonomous planetary navigation
No full textDeep Space missions require dedicated facilities to determine spacecraft's attitude and orbit, facing large operation costs, bearing the risk of signal disturbance/blockage in emergency situations, the delay caused by the roundtrip light-time, and the time required to process the data. For these reasons improving spacecraft autonomy is a crucial aspect for current and future space projects. To tackle these issues, the paper proposes a highly reliable crater detector that exploits the properties of generalizing of modern deep learning architectures. The detector is structured through a Cascade Mask R-CNN architecture, in which stages deeper into the cascade are more selective against close false positives because are trained sequentially, using the output of one stage to train the next. The backbone is ResNet-50, a 50 layers deep convolutional neural network that is able to avoid the vanishing gradient problem, which is coupled with Feature Pyramid Network, i.e. a pyramidal structure for the extraction of features at different scales to detect craters of different dimensions. In this study a database has been developed by manually labelling in COCO format using both circles and ellipsis. To train and test the Crater Detection Algorithm (CDA) 804 images have been obtained from the Wide Angle Camera global mosaic of the Lunar Reconnaissance Orbiter (LRO). Images, in orthographic projection show a resolution of 128.00 px/deg and 256.00 px/deg, respectively in latitude and longitude. From each picture a smaller squared area has been considered to avoid distortions at the edges and 134 tiles of 512x512 pixels have been obtained and manually labeled. Using grid space search to tune the parameters an encouraging mAP50 value of 77.8 has been accomplished. The CDA has been tested on a wide set of images to prove the accuracy and generalization of the detector under various challenging conditions. Namely, i) images of equatorial and polar regions of the Moon (considering a brightness variation of +50% and -50%), ii) distorted and blurred photos captured from the Narrow Angle Camera of the LRO and iii) pictures taken from missions MARS Reconnaissance Orbiter and MESSENGER observing respectively Mars and Mercury. Results prove the higher detection rates against the U-Net based architecture, adopted in recent crater detection studies. This detector is thus suitable for visual-based Terrain Relative Navigation in GNSS-denied planetary applications
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