Trajectory Visibility

We study the problem of testing whether there exists a time at which two entities moving along different piece-wise linear trajectories among polygonal obstacles are mutually visible. We study several variants, depending on whether or not the obstacles form a simple polygon, trajectories may intersect the polygon edges, and both or only one of the entities are moving. For constant complexity trajectories contained in a simple polygon with n vertices, we provide an (n) time algorithm to test if there is a time at which the entities can see each other. If the polygon contains holes, we present an (n log n) algorithm. We show that this is tight. We then consider storing the obstacles in a data structure, such that queries consisting of two line segments can be efficiently answered. We show that for all variants it is possible to answer queries in sublinear time using polynomial space and preprocessing time. As a critical intermediate step, we provide an efficient solution to a problem of independent interest: preprocess a convex polygon such that we can efficiently test intersection with a quadratic curve segment. If the obstacles form a simple polygon, this allows us to answer visibility queries in (n³/4log³ n) time using (nlog⁵ n) space. For more general obstacles the query time is (log^k n), for a constant but large value k, using (n^{3k}) space. We provide more efficient solutions when one of the entities remains stationary

Trajectory Range Visibility

We study the problem of Trajectory Range Visibility, determining the sub-trajectories on which two moving entities become mutually visible. Specifically, we consider two moving entities with not necessarily equal velocities and moving on a given piece-wise linear trajectory inside a simple polygon. Deciding whether the entities can see one another with given constant velocities, and assuming the trajectories only as line segments, was solved by P. Eades et al. in 2020. However, we obtain stronger results and support queries on constant velocities for non-constant complexity trajectories. Namely, given a constant query velocity for a moving entity, we specify all visible parts of the other entity's trajectory and all possible constant velocities of the other entity to become visible. Regarding line-segment trajectories, we obtain $\mathcal{O}(n \log n)$ time to specify all pairs of mutually visible sub-trajectories s.t. $n$ is the number of vertices of the polygon. Moreover, our results for a restricted case on non-constant complexity trajectories yield $\mathcal{O}(n + m(\log m + \log n))$ time, in which $m$ is the overall number of vertices of both trajectories. Regarding the unrestricted case, we provide $\mathcal{O}(n \log n + m(\log m + \log n))$ running time. We offer $\mathcal{O}(\log n)$ query time for line segment trajectories and $\mathcal{O}(\log m + k)$ for the non-constant complexity ones s.t. $k$ is the number of velocity ranges reported in the answer. Interestingly, our results require only $\mathcal{O}(n + m)$ space for non-constant complexity trajectories

Auto-ID enabled tracking and tracing data sharing over dynamic B2B and B2G relationships

RFID 2011 collocated with the 2011 IEEE MTT-S International Microwave Workshop Series on Millimeter Wave Integration Technologies (IMWS 2011)Growing complexity and uncertainty are still the key challenges enterprises are facing in managing and re-engineering their existing supply chains. To tackle these challenges, they are continuing innovating management practices and piloting emerging technologies for achieving supply chain visibility, agility, adaptability and security. Nowadays, subcontracting has already become a common practice in modern logistics industry through partnership establishment between the involved stakeholders for delivering consignments from a consignor to a consignee. Companies involved in international supply chain are piloting various supply chain security and integrity initiatives promoted by customs to establish trusted business-to-customs partnership for facilitating global trade and cutting out avoidable supply chain costs and delays due to governmental regulations compliance and unnecessary customs inspection. While existing Auto-ID enabled tracking and tracing solutions are promising for implementing these practices, they provide few efficient privacy protection mechanisms for stakeholders involved in the international supply chain to communicate logistics data over dynamic business-to-business and business-government relationships. A unified privacy protection mechanism is proposed in this work to fill in this gap. © 2011 IEEE.published_or_final_versio

KOLAM : human computer interfaces fro visual analytics in big data imagery

In the present day, we are faced with a deluge of disparate and dynamic information from multiple heterogeneous sources. Among these are the big data imagery datasets that are rapidly being generated via mature acquisition methods in the geospatial, surveillance (specifically, Wide Area Motion Imagery or WAMI) and biomedical domains. The need to interactively visualize these imagery datasets by using multiple types of views (as needed) into the data is common to these domains. Furthermore, researchers in each domain have additional needs: users of WAMI datasets also need to interactively track objects of interest using algorithms of their choice, visualize the resulting object trajectories and interactively edit these results as needed. While software tools that fulfill each of these requirements individually are available and well-used at present, there is still a need for tools that can combine the desired aspects of visualization, human computer interaction (HCI), data analysis, data management, and (geo-)spatial and temporal data processing into a single flexible and extensible system. KOLAM is an open, cross-platform, interoperable, scalable and extensible framework for visualization and analysis that we have developed to fulfil the above needs. The novel contributions in this thesis are the following: 1) Spatio-temporal caching for animating both giga-pixel and Full Motion Video (FMV) imagery, 2) Human computer interfaces purposefully designed to accommodate big data visualization, 3) Human-in-the-loop interactive video object tracking - ground-truthing of moving objects in wide area imagery using algorithm assisted human-in-the-loop coupled tracking, 4) Coordinated visualization using stacked layers, side-by-side layers/video sub-windows and embedded imagery, 5) Efficient one-click manual tracking, editing and data management of trajectories, 6) Efficient labeling of image segmentation regions and passing these results to desired modules, 7) Visualization of image processing results generated by non-interactive operators using layers, 8) Extension of interactive imagery and trajectory visualization to multi-monitor wall display environments, 9) Geospatial applications: Providing rapid roam, zoom and hyper-jump spatial operations, interactive blending, colormap and histogram enhancement, spherical projection and terrain maps, 10) Biomedical applications: Visualization and target tracking of cell motility in time-lapse cell imagery, collecting ground-truth from experts on whole-slide imagery (WSI) for developing histopathology analytic algorithms and computer-aided diagnosis for cancer grading, and easy-to-use tissue annotation features.Includes bibliographical reference

Ocean Color Science Working Group Highlights

No abstract availabl

Ocean Color Science Working Group

No abstract availabl

Liikepohjainen robotin moniaistijärjestelmän ekstrinsisten parametrien kalibrointi

This work presents an extrinsic parameter calibration of two Light Detection And Ranging (LiDAR) sensors fitted in a mobile robot platform. The LiDARs do not see each other, and are connected to separate computers. The calibration method is motion-based, targetless, data driven and requires no a priori information about the system. The motivation of the work comes from the need of a robust and accurate method for extrinsic parameter calibration, that is not dependent on specific calibration location, calibration target, or any a priori information about the system itself. This makes the calibration method usable in situations, where the system to be calibrated cannot easily be moved to a specific calibration location, or the calibration targets are infeasible to move in the sight of the sensors. The initial guesses for the parameters can be hard to obtain if the system is large, or the sensors hard to reach. The method described in this work supports multimodal sensors, and is not restricted to using LiDAR sensors. The extrinsic parameters of the LiDARs sensors are estimated in relation to the robot’s base link. First, the base link trajectory is estimated using an extended Kalman filter based algorithm that uses an Real-Time Kinematic (RTK) GNSS, an Inertial Measurement Unit (IMU) and wheel encoders. Second, the LiDAR trajectories are estimated using a normal distributions transform (NDT)-based registration approach. The time offset between the two computers is estimated from the estimated rotational magnitudes, and outliers are filtered from the data. Finally, the data are sampled to provide pose pairs, from which the extrinsic parameters are estimated using a cascading optimization, where a genetic algorithm optimizes the initial values of a gradient descent algorithm, and uses the output of the gradient descent algorithm as its cost function. The gradient descent algorithm optimizes the extrinsic parameters using the difference of the changes of the pose estimates as its cost function. The estimation accuracy is comparable with other motion-based calibration methods. The rotation parameter estimates are accurate, whereas the translation parameter estimates are coarse. This problem is solved by using the measured values for the translation parameters. The results are found to be roughly in line with the state-of-the-art motion-based calibration methods.Työssä kalibroidaan kahden mobiilirobottiin asennetun Light Detection And Ranging (LiDAR)-sensorin ulkoiset parametrit. Sensorit eivät näe toisiaan, ja ne ovat kytketty erillisiin tietokoneisiin. Kuvailtu kalibraatiometodi on liikepohjainen, kalibraatiokohteeton, datapohjainen, eikä vaadi a priori -tietoa järjestelmästä. Työ tehtiin, sillä tarkkaa ja robustia kalibraatiometodia, joka ei ole riippuvainen kalibraatioympäristöstä, kalibraatiokohteesta, taikka a priori -tiedosta, ei ole helposti saatavilla. Tällainen kalibraatiomenetelmä on erityisen käyttökelpoinen tilanteissa, joissa kalibroitavaa järjestelmää on vaikea liikuttaa kalibraatiota varten luotuun ympäristöön, tai jos kalibraatiokohteen liikuttaminen sensoreiden näkökentässä on vaikeaa tai mahdotonta. Myös a priori -tiedon, kuten alkuarvausten, saaminen voi olla vaikeaa tilanteissa, joissa kalibroitava järjestelmä on suuri, tai jos sensorit ovat vaikeasti saavutettavissa. Työssä kuvailtu metodi toimii multimodaalisilla sensoreilla, eikä ole ainoastaan käyttökelpoinen LiDAReilla. LiDARien ulkoiset parametrit estimoidaan suhteessa robotin keskipisteeseen. Robotin keskipisteen liikerata estimoidaan käyttäen algoritmia, joka perustuu laajennettuun Kalmansuotimeen. Kalmansuodin yhdistää dataa Real-Time Kinematic (RTK) GNSS -vastaanottimesta, kiihtyvyysanturista sekä robotin pyörien enkoodereista. LiDARien liikeradat estimoidaan normal distributions transform (NDT) -pohjaista rekisteröintimenetelmää käyttäen. Aikaviive kahden tietokoneen välillä estimoidaan vertailemalla estimoitujen liikeratojen rotaatioiden suuruuksia. Dataa suodatetaan poistaen siitä mittausvirheitä ja kohinaa. Lopuksi datasta valikoidaan asentopareja, joista ulkoiset parametrit estimoidaan käyttäen sisäkkäistä optimointia, jossa geneettinen algoritmi optimoi gradienttimenetelmän alkuarvoja, käyttäen gradienttimenetelmän lopputulosta kustannusfunktionaan. Gradienttimenetelmä minimoi ulkoisten parametrien estimaattien virhettä käyttäen liikeratojen muutosten eroa kustannusfunktionaan. Parametrien estimaattien tarkkuus on vertailukelpoista muiden liikepohjaisten kalibraatiomenetelmien kanssa. Rotaatioparametrien estimaatit ovat tarkkoja, kun taas translaatioparametrien estimaatit ovat suuntaa-antavia. Tarkkuusongelma ratkaistaan käyttämällä translaatioparametreinä niiden mitattuja arvoja. Työn tuloksien tarkkuuden todetaan olevan karkeasti muiden modernien liikepohjaisten kalibraatiomenetelmien tasolla

Analyse et détection des trajectoires d'approches atypiques des aéronefs à l'aide de l'analyse de données fonctionnelles et de l'apprentissage automatique

L'amélioration de la sécurité aérienne implique généralement l'identification, la détection et la gestion des événements indésirables qui peuvent conduire à des événements finaux mortels. De précédentes études menées par la DSAC, l'autorité de surveillance française, ont permis d'identifier les approches non-conformes présentant des déviations par rapport aux procédures standards comme des événements indésirables. Cette thèse vise à explorer les techniques de l'analyse de données fonctionnelles et d'apprentissage automatique afin de fournir des algorithmes permettant la détection et l'analyse de trajectoires atypiques en approche à partir de données sol. Quatre axes de recherche sont abordés. Le premier axe vise à développer un algorithme d'analyse post-opérationnel basé sur des techniques d'analyse de données fonctionnelles et d'apprentissage non-supervisé pour la détection de comportements atypiques en approche. Le modèle sera confronté à l'analyse des bureaux de sécurité des vols des compagnies aériennes, et sera appliqué dans le contexte particulier de la période COVID-19 pour illustrer son utilisation potentielle alors que le système global ATM est confronté à une crise. Le deuxième axe de recherche s'intéresse plus particulièrement à la génération et à l'extraction d'informations à partir de données radar à l'aide de nouvelles techniques telles que l'apprentissage automatique. Ces méthodologies permettent d'améliorer la compréhension et l'analyse des trajectoires, par exemple dans le cas de l'estimation des paramètres embarqués à partir des paramètres radar. Le troisième axe, propose de nouvelles techniques de manipulation et de génération de données en utilisant le cadre de l'analyse de données fonctionnelles. Enfin, le quatrième axe se concentre sur l'extension en temps réel de l'algorithme post-opérationnel grâce à l'utilisation de techniques de contrôle optimal, donnant des pistes vers de nouveaux systèmes d'alerte permettant une meilleure conscience de la situation.Improving aviation safety generally involves identifying, detecting and managing undesirable events that can lead to final events with fatalities. Previous studies conducted by the French National Supervisory Authority have led to the identification of non-compliant approaches presenting deviation from standard procedures as undesirable events. This thesis aims to explore functional data analysis and machine learning techniques in order to provide algorithms for the detection and analysis of atypical trajectories in approach from ground side. Four research directions are being investigated. The first axis aims to develop a post-op analysis algorithm based on functional data analysis techniques and unsupervised learning for the detection of atypical behaviours in approach. The model is confronted with the analysis of airline flight safety offices, and is applied in the particular context of the COVID-19 crisis to illustrate its potential use while the global ATM system is facing a standstill. The second axis of research addresses the generation and extraction of information from radar data using new techniques such as Machine Learning. These methodologies allow to \mbox{improve} the understanding and the analysis of trajectories, for example in the case of the estimation of on-board parameters from radar parameters. The third axis proposes novel data manipulation and generation techniques using the functional data analysis framework. Finally, the fourth axis focuses on extending the post-operational algorithm into real time with the use of optimal control techniques, giving directions to new situation awareness alerting systems