1,721,015 research outputs found
Calibration procedures of a vision-based system for relative motion estimation between satellites flying in proximity
No full textRelative Measurements systems represent a key technology for next generation space missions that require proximity operations between satellites. Before on-orbit validation, the sensors and algorithms need to be validated in laboratory employing a good fiducial reference of the relative motion and specific calibration procedures concerning the estimation of roto-translation matrices between different reference frames. This paper presents a set of calibration procedures that allow to assess the accuracy in estimating the relative pose between a Target spacecraft, equipped with a set of square markers, and an Inspector satellite moving in proximity and hosting a monocular camera. An external Motion Capture system is used to track the motion of a set of spherical markers attached to both the Target and the Inspector, providing a reliable fiducial reference for the relative pose between the two spacecraft. The proposed calibration procedures were tested using the SPARTANS hardware facility of the University of Padova
3D Radiometric Mapping by Means of LiDAR SLAM and Thermal Camera Data Fusion
Full text linkThe ability to produce 3D maps with infrared radiometric information is of great interest for many applications, such as rover navigation, industrial plant monitoring, and rescue robotics. In this paper, we present a system for large-scale thermal mapping based on IR thermal images and 3D LiDAR point cloud data fusion. The alignment between the point clouds and the thermal images is carried out using the extrinsic camera-to-LiDAR parameters, obtained by means of a dedicated calibration process. Rover’s trajectory, which is necessary for point cloud registration, is obtained by means of a LiDAR Simultaneous Localization and Mapping (SLAM) algorithm. Finally, the registered and merged thermal point clouds are represented through an OcTree data structure, where each voxel is associated with the average temperature of the 3D points contained within. Furthermore, the paper presents in detail the method for determining extrinsic parameters, which is based on the identification of a hot cardboard box. Both methods were validated in a laboratory environment and outdoors. It is shown that the developed system is capable of locating a thermal object with an accuracy of up to 9 cm in a 45 m map size with a voxelization of 14 cm
Occupancy grid mapping for rover navigation based on semantic segmentation
Full text linkObstacle mapping is a fundamental building block of the autonomous navigation pipeline of many robotic platforms such as planetary rovers. Nowadays, occupancy grid mapping is a widely used tool for obstacle perception. It foreseen the representation of the environment in evenly spaced cells, whose posterior probability of being occupied is updated based on range sensors measurement. In more classic approaches, the cells are updated to occupied at the point where the ray emitted by the range sensor encounters an obstacle, such as a wall. The main limitation of this kind of methods is that they are not able to identify planar obstacles, such as slippery, sandy, or rocky soils. In this work, we use the measurements of a stereo camera combined with a pixel labeling technique based on Convolution Neural Networks to identify the presence of rocky obstacles in planetary environment. Once identified, the obstacles are converted into a scan-like model. The estimation of the relative pose between successive frames is carried out using ORB-SLAM algorithm. The final step consists of updating the occupancy grid map using the Bayes' update Rule. To evaluate the metrological performances of the proposed method images from the Martian analogous dataset, the ESA Katwijk Beach Planetary Rover Dataset have been used. The evaluation has been performed by comparing the generated occupancy map with a manually segmented ortomosaic map, obtained by drones' survey of the area used as reference
The Metric Equivalent in Poggioli’s “Rhythmic Versions” from Pushkin, Tjutchev, Pasternak, and Akhmatova Firenze, , 2012: 89-101.
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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
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Improving Keypoints Tracking With Machine-Learned Features in Event-Camera-Based Visual Odometry
No full textThis paper introduces a feature detection method developed through machine learning specifically tailored for event-camera based visual odometry techniques used in reconstructing trajectories for unmanned aerial vehicles. The proposed approach leverages machine-learned features to improve the precision of trajectory reconstruction. Unlike conventional visual odometry methods, which often struggle in low light and high-speed scenarios, the event-camera-based method addresses these challenges by focusing solely on detecting and processing changes in the visual scene. The machine-learned features are designed to capture the distinctive attributes of event-camera data, thereby refining the accuracy of trajectory reconstruction. The inference pipeline consists of a module that is iterated twice sequentially, comprising a Squeeze-and-Excite block and a ConvLSTM block with residual connection. This is succeeded by a final convolutional layer that generates trajectory information for corners in the form of heatmap sequences. In the experimental phase, a series of images was gathered using an event-camera in outdoor settings for both training and testing purposes
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