6 research outputs found
Visual Place Recognition for Autonomous Robots
Autonomous robotics has been the subject of great interest within the research community over the past few decades. Its applications are wide-spread, ranging from health-care to manufacturing, goods transportation to home deliveries, site-maintenance to construction, planetary explorations to rescue operations and many others, including but not limited to agriculture, defence, commerce, leisure and extreme environments. At the core of robot autonomy lies the problem of localisation, i.e, knowing where it is and within the robotics community, this problem is termed as place recognition. Place recognition using only visual input is termed as Visual Place Recognition (VPR) and refers to the ability of an autonomous system to recall a previously visited place using only visual input, under changing viewpoint, illumination and seasonal conditions, and given computational and storage constraints.
This thesis is a collection of 4 inter-linked, mutually-relevant but branching-out topics within VPR: 1) What makes a place/image worthy for VPR?, 2) How to define a state-of-the-art in VPR?, 3) Do VPR techniques designed for ground-based platforms extend to aerial platforms? and 4) Can a handcrafted VPR technique outperform deep-learning-based VPR techniques? Each of these questions is a dedicated, peer-reviewed chapter in this thesis and the author attempts to answer these questions to the best of his abilities.
The worthiness of a place essentially refers to the salience and distinctiveness of the content in the image of this place. This salience is modelled as a framework, namely memorable-maps, comprising of 3 conjoint criteria: a) Human-memorability of an image, 2) Staticity and 3) Information content. Because a large number of VPR techniques have been proposed over the past 10-15 years, and due to the variation of employed VPR datasets and metrics for evaluation, the correct state-of-the-art remains ambiguous. The author levels this playing field by deploying 10 contemporary techniques on a common platform and use the most challenging VPR datasets to provide a holistic performance comparison. This platform is then extended to aerial place recognition datasets to answer the 3rd question above. Finally, the author designs a novel, handcrafted, compute-efficient and training-free VPR technique that outperforms state-of-the-art VPR techniques on 5 different VPR datasets
On the relationship between neuronal codes and mental models
Das ĂŒbergeordnete Ziel meiner Arbeit an dieser Dissertation
war ein besseres VerstÀndnis des Zusammenhangs
von mentalen Modellen
und den zugrundeliegenden Prinzipien,
die zur Selbstorganisation neuronaler Verschaltung fĂŒhren.
Die Dissertation besteht aus vier individuellen Publikationen,
die dieses Ziel aus unterschiedlichen Perspektiven angehen.
WÀhrend die Selbstorganisation von Sparse-Coding-ReprÀsentationen
in neuronalem Substrat
bereits ausgiebig untersucht worden ist,
sind viele Forschungsfragen dazu,
wie Sparse-Coding fĂŒr höhere, kognitive Prozesse genutzt werden könnte
noch offen.
Die ersten zwei Studien,
die in Kapitel 2 und Kapitel 3 enthalten sind,
behandeln die Frage,
inwieweit ReprÀsentationen, die mit Sparse-Coding entstehen,
mentalen Modellen entsprechen.
Wir haben folgende SelektivitÀten
in Sparse-Coding-ReprÀsentationen identifiziert:
mit Stereo-Bildern als Eingangsdaten
war die ReprĂ€sentation selektiv fĂŒr die DisparitĂ€ten von Bildstrukturen,
welche fĂŒr das AbschĂ€tzen der Entfernung der Strukturen zum Beobachter genutzt werden können.
AuĂerdem war die ReprĂ€sentation selektiv fĂŒr die die vorherrschende Orientierung in Texturen,
was fĂŒr das AbschĂ€tzen der Neigung von OberflĂ€chen genutzt werden kann.
Mit optischem Fluss von Eigenbewegung als Eingangsdaten
war die ReprĂ€sentation selektiv fĂŒr die Richtung der Eigenbewegung
in den sechs Freiheitsgraden.
Wegen des direkten Zusammenhangs der SelektivitÀten mit physikalischen Eigenschaften
können ReprÀsentationen, die mit Sparse-Coding entstehen,
als frĂŒhe sensorische Modelle der Umgebung dienen.
Die kognitiven Prozesse hinter rÀumlichem Wissen
ruhen auf mentalen Modellen, welche die Umgebung representieren.
Wir haben in der dritten Studie,
welche in Kapitel 4 enthalten ist,
ein topologisches Modell zur Navigation prÀsentiert,
Es beschreibt einen dualen Populations-Code,
bei dem der erste Populations-Code Orte anhand von Orts-Feldern (Place-Fields) kodiert
und der zweite Populations-Code Bewegungs-Instruktionen,
basierend auf der VerknĂŒpfung von Orts-Feldern, kodiert.
Der Fokus lag nicht auf der Implementation in biologischem Substrat
oder auf einer exakten Modellierung physiologischer Ergebnisse.
Das Modell ist eine biologisch plausible, einfache Methode zur Navigation,
welche sich an einen Zwischenschritt emergenter Navigations-FĂ€higkeiten
in einer evolutiven Navigations-Hierarchie annÀhert.
Unser automatisierter Test der Sehleistungen von MĂ€usen,
welcher in Kapitel 5 beschrieben wird,
ist ein Beispiel von Verhaltens-Tests
im Wahrnehmungs-Handlungs-Zyklus (Perception-Action-Cycle).
Das Ziel dieser Studie war die Quantifizierung des optokinetischen Reflexes.
Wegen des reichhaltigen Verhaltensrepertoires von MĂ€usen
sind fĂŒr die Quantifizierung viele umfangreiche Analyseschritte erforderlich.
Tiere und Menschen sind verkörperte (embodied) lebende Systeme
und daher aus stark miteinander verwobenen Modulen oder EntitÀten zusammengesetzt,
welche auĂerdem auch mit der Umgebung verwoben sind.
Um lebende Systeme als Ganzes zu studieren
ist es notwendig Hypothesen,
zum Beispiel zur Natur mentaler Modelle,
im Wahrnehmungs-Handlungs-Zyklus zu testen.
Zusammengefasst erweitern die Studien dieser Dissertation
unser VerstĂ€ndnis des Charakters frĂŒher sensorischer ReprĂ€sentationen als mentale Modelle,
sowie unser VerstĂ€ndnis höherer, mentalen Modellen fĂŒr die rĂ€umliche Navigation.
DarĂŒber hinaus enthĂ€lt es ein Beispiel
fĂŒr das Evaluieren von Hypothesn im Wahr\-neh\-mungs-Handlungs-Zyklus.The superordinate aim of my work towards this thesis
was a better understanding
of the relationship between mental models
and the underlying principles that lead to the self-organization
of neuronal circuitry.
The thesis consists of four individual publications,
which approach this goal from differing perspectives.
While the formation of sparse coding representations in neuronal substrate
has been investigated extensively,
many research questions
on how sparse coding may be exploited for higher cognitive processing
are still open.
The first two studies,
included as chapter 2 and chapter 3,
asked to what extend representations obtained with sparse coding
match mental models.
We identified the following selectivities in sparse coding representations:
with stereo images as input,
the representation was selective for the disparity of image structures,
which can be used to infer the distance of structures to the observer.
Furthermore, it was selective to the predominant orientation in textures,
which can be used to infer the orientation of surfaces.
With optic flow from egomotion as input,
the representation was selective to the direction of egomotion
in 6 degrees of freedom.
Due to the direct relation between selectivity and physical properties,
these representations, obtained with sparse coding,
can serve as early sensory models of the environment.
The cognitive processes behind spatial knowledge
rest on mental models that represent the environment.
We presented a topological model for wayfinding
in the third study,
included as chapter 4.
It describes a dual population code,
where the first population code encodes places
by means of place fields,
and the second population code encodes motion instructions
based on links between place fields.
We did not focus on an implementation in biological substrate
or on an exact fit to physiological findings.
The model is a biologically plausible, parsimonious method for wayfinding,
which may be close to an intermediate step
of emergent skills in an evolutionary navigational hierarchy.
Our automated testing for visual performance in mice,
included in chapter 5,
is an example of behavioral testing in the perception-action cycle.
The goal of this study was to quantify the optokinetic reflex.
Due to the rich behavioral repertoire of mice,
quantification required many elaborate steps of computational analyses.
Animals and humans are embodied living systems,
and therefore composed of strongly enmeshed modules or entities,
which are also enmeshed with the environment.
In order to study living systems as a whole,
it is necessary to test hypothesis,
for example on the nature of mental models,
in the perception-action cycle.
In summary,
the studies included in this thesis
extend our view on the character of early sensory representations
as mental models,
as well as on high-level mental models
for spatial navigation.
Additionally it contains an example
for the evaluation of hypotheses in the perception-action cycle
Combining SOA and BPM Technologies for Cross-System Process Automation
This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation
Review of Particle Physics
The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,143 new measurements from 709 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Among the 120 reviews are many that are new or heavily revised, including a new review on Machine Learning, and one on Spectroscopy of Light Meson Resonances.
The Review is divided into two volumes. Volume 1 includes the Summary Tables and 97 review articles. Volume 2 consists of the Particle Listings and contains also 23 reviews that address specific aspects of the data presented in the Listings
Review of Particle Physics
The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,143 new measurements from 709 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Among the 120 reviews are many that are new or heavily revised, including a new review on Machine Learning, and one on Spectroscopy of Light Meson Resonances.
The Review is divided into two volumes. Volume 1 includes the Summary Tables and 97 review articles. Volume 2 consists of the Particle Listings and contains also 23 reviews that address specific aspects of the data presented in the Listings.
The complete Review (both volumes) is published online on the website of the Particle Data Group (pdg.lbl.gov) and in a journal. Volume 1 is available in print as the PDG Book. A Particle Physics Booklet with the Summary Tables and essential tables, figures, and equations from selected review articles is available in print, as a web version optimized for use on phones, and as an Android app
Review of Particle Physics
The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,143
new measurements from 709 papers, we list, evaluate, and average measured properties of gauge bosons and the
recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical
particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search
limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs
Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology,
Particle Detectors, Colliders, Probability and Statistics. Among the 120 reviews are many that are new or heavily
revised, including a new review on Machine Learning, and one on Spectroscopy of Light Meson Resonances.
The Review is divided into two volumes. Volume 1 includes the Summary Tables and 97 review articles. Volume
2 consists of the Particle Listings and contains also 23 reviews that address specific aspects of the data presented
in the Listings.
The complete Review (both volumes) is published online on the website of the Particle Data Group (pdg.lbl.gov)
and in a journal. Volume 1 is available in print as the PDG Book. A Particle Physics Booklet with the Summary
Tables and essential tables, figures, and equations from selected review articles is available in print, as a web version
optimized for use on phones, and as an Android app.United States Department of Energy (DOE) DE-AC02-05CH11231government of Japan (Ministry of Education, Culture, Sports, Science and Technology)Istituto Nazionale di Fisica Nucleare (INFN)Physical Society of Japan (JPS)European Laboratory for Particle Physics (CERN)United States Department of Energy (DOE