71 research outputs found
RANSAC for Robotic Applications: A Survey
Random Sample Consensus, most commonly abbreviated as RANSAC, is a robust estimation method for the parameters of a model contaminated by a sizable percentage of outliers. In its simplest form, the process starts with a sampling of the minimum data needed to perform an estimation, followed by an evaluation of its adequacy, and further repetitions of this process until some stopping criterion is met. Multiple variants have been proposed in which this workflow is modified, typically tweaking one or several of these steps for improvements in computing time or the quality of the estimation of the parameters. RANSAC is widely applied in the field of robotics, for example, for finding geometric shapes (planes, cylinders, spheres, etc.) in cloud points or for estimating the best transformation between different camera views. In this paper, we present a review of the current state of the art of RANSAC family methods with a special interest in applications in robotics.This work has been partially funded by the Basque Government, Spain, under Research Teams Grant number IT1427-22 and under ELKARTEK LANVERSO Grant number KK-2022/00065; the Spanish Ministry of Science (MCIU), the State Research Agency (AEI), the European Regional Development Fund (FEDER), under Grant number PID2021-122402OB-C21 (MCIU/AEI/FEDER, UE); and the Spanish Ministry of Science, Innovation and Universities, under Grant FPU18/04737
UAVs for the Environmental Sciences
This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application
UAV or Drones for Remote Sensing Applications in GPS/GNSS Enabled and GPS/GNSS Denied Environments
The design of novel UAV systems and the use of UAV platforms integrated with robotic sensing and imaging techniques, as well as the development of processing workflows and the capacity of ultra-high temporal and spatial resolution data, have enabled a rapid uptake of UAVs and drones across several industries and application domains.This book provides a forum for high-quality peer-reviewed papers that broaden awareness and understanding of single- and multiple-UAV developments for remote sensing applications, and associated developments in sensor technology, data processing and communications, and UAV system design and sensing capabilities in GPS-enabled and, more broadly, Global Navigation Satellite System (GNSS)-enabled and GPS/GNSS-denied environments.Contributions include:UAV-based photogrammetry, laser scanning, multispectral imaging, hyperspectral imaging, and thermal imaging;UAV sensor applications; spatial ecology; pest detection; reef; forestry; volcanology; precision agriculture wildlife species tracking; search and rescue; target tracking; atmosphere monitoring; chemical, biological, and natural disaster phenomena; fire prevention, flood prevention; volcanic monitoring; pollution monitoring; microclimates; and land use;Wildlife and target detection and recognition from UAV imagery using deep learning and machine learning techniques;UAV-based change detection
Method of optimization of the fundamental matrix by technique speeded up robust features application of different stress images
The purpose of determining the fundamental matrix (F) is to define the epipolar geometry and to relate two 2D images of the same scene or video series to find the 3D scenes. The problem we address in this work is the estimation of the localization error and the processing time. We start by comparing the following feature extraction techniques: Harris, features from accelerated segment test (FAST), scale invariant feature transform (SIFT) and speed-up robust features (SURF) with respect to the number of detected points and correct matches by different changes in images. Then, we merged the best chosen by the objective function, which groups the descriptors by different regions in order to calculate âFâ. Then, we applied the standardized eight-point algorithm which also automatically eliminates the outliers to find the optimal solution âFâ. The test of our optimization approach is applied on the real images with different scene variations. Our simulation results provided good results in terms of accuracy and the computation time of âFâ does not exceed 900 ms, as well as the projection error of maximum 1 pixel, regardless of the modification
Medical SLAM in an autonomous robotic system
One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-operative morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeonâs navigation capabilities by observing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted instruments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This thesis addresses the ambitious goal of achieving surgical autonomy, through the study of the anatomical environment by Initially studying the technology present and what is needed to analyze the scene: vision sensors. A novel endoscope for autonomous surgical task execution is presented in the first part of this thesis. Which combines a standard stereo camera with a depth sensor. This solution introduces several key advantages, such as the possibility of reconstructing the 3D at a greater distance than traditional endoscopes. Then the problem of hand-eye calibration is tackled, which unites the vision system and the robot in a single reference system. Increasing the accuracy in the surgical work plan. In the second part of the thesis the problem of the 3D reconstruction and the algorithms currently in use were addressed. In MIS, simultaneous localization and mapping (SLAM) can be used to localize the pose of the endoscopic camera and build ta 3D model of the tissue surface. Another key element for MIS is to have real-time knowledge of the pose of surgical tools with respect to the surgical camera and underlying anatomy. Starting from the ORB-SLAM algorithm we have modified the architecture to make it usable in an anatomical environment by adding the registration of the pre-operative information of the intervention to the map obtained from the SLAM. Once it has been proven that the slam algorithm is usable in an anatomical environment, it has been improved by adding semantic segmentation to be able to distinguish dynamic features from static ones. All the results in this thesis are validated on training setups, which mimics some of the challenges of real surgery and on setups that simulate the human body within Autonomous Robotic Surgery (ARS) and Smart Autonomous Robotic Assistant Surgeon (SARAS) projects
Medical SLAM in an autonomous robotic system
One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-operative morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeonâs navigation capabilities by observing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted instruments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This thesis addresses the ambitious goal of achieving surgical autonomy, through the study of the anatomical environment by Initially studying the technology present and what is needed to analyze the scene: vision sensors. A novel endoscope for autonomous surgical task execution is presented in the first part of this thesis. Which combines a standard stereo camera with a depth sensor. This solution introduces several key advantages, such as the possibility of reconstructing the 3D at a greater distance than traditional endoscopes. Then the problem of hand-eye calibration is tackled, which unites the vision system and the robot in a single reference system. Increasing the accuracy in the surgical work plan. In the second part of the thesis the problem of the 3D reconstruction and the algorithms currently in use were addressed. In MIS, simultaneous localization and mapping (SLAM) can be used to localize the pose of the endoscopic camera and build ta 3D model of the tissue surface. Another key element for MIS is to have real-time knowledge of the pose of surgical tools with respect to the surgical camera and underlying anatomy. Starting from the ORB-SLAM algorithm we have modified the architecture to make it usable in an anatomical environment by adding the registration of the pre-operative information of the intervention to the map obtained from the SLAM. Once it has been proven that the slam algorithm is usable in an anatomical environment, it has been improved by adding semantic segmentation to be able to distinguish dynamic features from static ones. All the results in this thesis are validated on training setups, which mimics some of the challenges of real surgery and on setups that simulate the human body within Autonomous Robotic Surgery (ARS) and Smart Autonomous Robotic Assistant Surgeon (SARAS) projects
Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020
On behalf of the Program Committee, a very warm welcome to the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020). This edition of the conference is held in Bologna and organised by the University of Bologna. The CLiC-it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after six years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges
Multimodal Navigation for Accurate Space Rendezvous Missions
© Cranfield University 2021. All rights reserved. No part of
this publication may be reproduced without the written
permission of the copyright ownerRelative navigation is paramount in space missions that involve rendezvousing
between two spacecraft. It demands accurate and continuous estimation of the six
degree-of-freedom relative pose, as this stage involves close-proximity-fast-reaction
operations that can last up to five orbits. This has been routinely achieved thanks to
active sensors such as lidar, but their large size, cost, power and limited operational
range remain a stumbling block for en masse on-board integration. With the onset
of faster processing units, lighter and cheaper passive optical sensors are emerging as
the suitable alternative for autonomous rendezvous in combination with computer
vision algorithms. Current vision-based solutions, however, are limited by adverse
illumination conditions such as solar glare, shadowing, and eclipse. These effects are
exacerbated when the target does not hold cooperative markers to accommodate the
estimation process and is incapable of controlling its rotational state.
This thesis explores novel model-based methods that exploit sequences of monoc ular images acquired by an on-board camera to accurately carry out spacecraft
relative pose estimation for non-cooperative close-range rendezvous with a known
artificial target. The proposed solutions tackle the current challenges of imaging in
the visible spectrum and investigate the contribution of the long wavelength infrared
(or âthermalâ) band towards a combined multimodal approach.
As part of the research, a visible-thermal synthetic dataset of a rendezvous
approach with the defunct satellite Envisat is generated from the ground up using a
realistic orbital camera simulator. From the rendered trajectories, the performance
of several state-of-the-art feature detectors and descriptors is first evaluated for
both modalities in a tailored scenario for short and wide baseline image processing
transforms. Multiple combinations, including the pairing of algorithms with their
non-native counterparts, are tested. Computational runtimes are assessed in an
embedded hardware board.
From the insight gained, a method to estimate the pose on the visible band is
derived from minimising geometric constraints between online local point and edge
contour features matched to keyframes generated offline from a 3D model of the
target. The combination of both feature types is demonstrated to achieve a pose
solution for a tumbling target using a sparse set of training images, bypassing the
need for hardware-accelerated real-time renderings of the model.
The proposed algorithm is then augmented with an extended Kalman filter
which processes each feature-induced minimisation output as individual pseudo measurements, fusing them to estimate the relative pose and velocity states at
each time-step. Both the minimisation and filtering are established using Lie group
formalisms, allowing for the covariance of the solution computed by the former to be automatically incorporated as measurement noise in the latter, providing
an automatic weighing of each feature type directly related to the quality of the
matches. The predicted states are then used to search for new feature matches in the
subsequent time-step. Furthermore, a method to derive a coarse viewpoint estimate
to initialise the nominal algorithm is developed based on probabilistic modelling of
the targetâs shape. The robustness of the complete approach is demonstrated for
several synthetic and laboratory test cases involving two types of target undergoing
extreme illumination conditions.
Lastly, an innovative deep learning-based framework is developed by processing
the features extracted by a convolutional front-end with long short-term memory cells,
thus proposing the first deep recurrent convolutional neural network for spacecraft
pose estimation. The framework is used to compare the performance achieved by
visible-only and multimodal input sequences, where the addition of the thermal band
is shown to greatly improve the performance during sunlit sequences. Potential
limitations of this modality are also identified, such as when the targetâs thermal
signature is comparable to Earthâs during eclipse.PH
Object Tracking
Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application
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