879 research outputs found
Globally-Coordinated Locally-Linear Modeling of Multi-Dimensional Data
This thesis considers the problem of modeling and analysis of continuous, locally-linear, multi-dimensional spatio-temporal data. Our work extends the previously reported theoretical work on the global coordination model to temporal analysis of continuous, multi-dimensional data. We have developed algorithms for time-varying data analysis and used them in full-scale, real-world applications. The applications demonstrated in this thesis include tracking, synthesis, recognitions and retrieval of dynamic objects based on their shape, appearance and motion. The proposed approach in this thesis has advantages over existing approaches to analyzing complex spatio-temporal data. Experiments show that the new modeling features of our approach improve the performance of existing approaches in many applications. In object tracking, our approach is the first one to track nonlinear appearance variations by using low-dimensional representation of the appearance change in globally-coordinated linear subspaces. In dynamic texture synthesis, we are able to model non-stationary dynamic textures, which cannot be handled by any of the existing approaches. In human motion synthesis, we show that realistic synthesis can be performed without using specific transition points, or key frames
SEGMENTATION, RECOGNITION, AND ALIGNMENT OF COLLABORATIVE GROUP MOTION
Modeling and recognition of human motion in videos has broad applications in behavioral biometrics, content-based visual data analysis, security and surveillance, as well as designing interactive environments. Significant progress has been made in the past two decades by way of new models, methods, and implementations. In this dissertation, we focus our attention on a relatively less investigated sub-area called collaborative group motion analysis. Collaborative group motions are those that typically involve multiple objects, wherein the motion patterns of individual objects may vary significantly in both space and time, but the collective motion pattern of the ensemble allows characterization in terms of geometry and statistics. Therefore, the motions or activities of an individual object constitute local information. A framework to synthesize all local information into a holistic view, and to explicitly characterize interactions among objects, involves large scale global reasoning, and is of significant complexity. In this dissertation, we first review relevant previous contributions on human motion/activity modeling and recognition, and then propose several approaches to answer a sequence of traditional vision questions including 1) which of the motion elements among all are the ones relevant to a group motion pattern of interest (Segmentation); 2) what is the underlying motion pattern (Recognition); and 3) how two motion ensembles are similar and how we can 'optimally' transform one to match the other (Alignment). Our primary practical scenario is American football play, where the corresponding problems are 1) who are offensive players; 2) what are the offensive strategy they are using; and 3) whether two plays are using the same strategy and how we can remove the spatio-temporal misalignment between them due to internal or external factors. The proposed approaches discard traditional modeling paradigm but explore either concise descriptors, hierarchies, stochastic mechanism, or compact generative model to achieve both effectiveness and efficiency.
In particular, the intrinsic geometry of the spaces of the involved features/descriptors/quantities is exploited and statistical tools are established on these nonlinear manifolds. These initial attempts have identified new challenging problems in complex motion analysis, as well as in more general tasks in video dynamics. The insights gained from nonlinear geometric modeling and analysis in this dissertation may hopefully be useful toward a broader class of computer vision applications
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Singularity-free Guiding Vector Field for Robot Navigation
Most of the existing path-following navigation algorithms cannot guarantee
global convergence to desired paths or enable following self-intersected
desired paths due to the existence of singular points where navigation
algorithms return unreliable or even no solutions. One typical example arises
in vector-field guided path-following (VF-PF) navigation algorithms. These
algorithms are based on a vector field, and the singular points are exactly
where the vector field diminishes. In this paper, we show that it is
mathematically impossible for conventional VF-PF algorithms to achieve global
convergence to desired paths that are self-intersected or even just simple
closed (precisely, homeomorphic to the unit circle). Motivated by this new
impossibility result, we propose a novel method to transform self-intersected
or simple closed desired paths to non-self-intersected and unbounded
(precisely, homeomorphic to the real line) counterparts in a higher-dimensional
space. Corresponding to this new desired path, we construct a singularity-free
guiding vector field on a higher-dimensional space. The integral curves of this
new guiding vector field is thus exploited to enable global convergence to the
higher-dimensional desired path, and therefore the projection of the integral
curves on a lower-dimensional subspace converge to the physical
(lower-dimensional) desired path. Rigorous theoretical analysis is carried out
for the theoretical results using dynamical systems theory. In addition, we
show both by theoretical analysis and numerical simulations that our proposed
method is an extension combining conventional VF-PF algorithms and trajectory
tracking algorithms. Finally, to show the practical value of our proposed
approach for complex engineering systems, we conduct outdoor experiments with a
fixed-wing airplane in windy environment to follow both 2D and 3D desired
paths.Comment: Accepted for publication in IEEE Trransactions on Robotics (T-RO
An integrated framework on characterization, control, and testing of an electrical turbocharger assist
Engine downsizing is a promising trend for
improving fuel efficiency of conventional powertrain vehicles. The reduced engine capacity can be compensated by better air delivery through electrically assisted boosting
systems, while the most critical technology is the electric turbocharger. In this paper, an integrated framework for characterization, control, and testing of the electric turbocharger
is proposed. Starting from a physical characterization of the engine, the impact of the electric turbocharger on fuel economy and exhaust emissions are both analyzed, as well as its controllability. A multi-variable robust controller is designed to regulate the dynamics of the electrified turbocharged engine in a systematic approach. To minimize the fuel consumption in real time, a supervisory
level controller is designed to update the setpoints of key controlled variables in an optimal way. Furthermore, a cutting-edge experimental platform of a heavy-duty electrified turbocharged diesel engine is built. The demonstrated excellent tracking performance, high robustness, and improvements on fuel efficiency in experimental results prove the effectiveness of both the developed system and the proposed control strategy
A Continuous Grasp Representation for the Imitation Learning of Grasps on Humanoid Robots
Models and methods are presented which enable a humanoid robot to learn reusable, adaptive grasping skills. Mechanisms and principles in human grasp behavior are studied. The findings are used to develop a grasp representation capable of retaining specific motion characteristics and of adapting to different objects and tasks. Based on the representation a framework is proposed which enables the robot to observe human grasping, learn grasp representations, and infer executable grasping actions
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