2,838 research outputs found
Cooperative Pursuit with Multi-Pursuer and One Faster Free-moving Evader
This paper addresses a multi-pursuer single-evader pursuit-evasion game where
the free-moving evader moves faster than the pursuers. Most of the existing
works impose constraints on the faster evader such as limited moving area and
moving direction. When the faster evader is allowed to move freely without any
constraint, the main issues are how to form an encirclement to trap the evader
into the capture domain, how to balance between forming an encirclement and
approaching the faster evader, and what conditions make the capture possible.
In this paper, a distributed pursuit algorithm is proposed to enable pursuers
to form an encirclement and approach the faster evader. An algorithm that
balances between forming an encirclement and approaching the faster evader is
proposed. Moreover, sufficient capture conditions are derived based on the
initial spatial distribution and the speed ratios of the pursuers and the
evader. Simulation and experimental results on ground robots validate the
effectiveness and practicability of the proposed method
Combining generalized linear models
Traditional data analysis techniques that depend on the selection of a model are vulnerable to model uncertainty. This thesis establishes some statistical properties of an alternative to model selection, a model combining method called Adaptive Regression by Mixing (ARM). This work implements and extensively studies ARM in the context of generalized linear models including ANOVA, loglinear and survival models.;We have found applications for the general idea of model combining in each of the three settings, and have derived the theoretical risk bound of the combined estimator in each.;In addition to demonstrating good theoretical properties and the empirical advantage of ARM in applications in all three settings, we have addressed specific issues and challenges posed by each setting. In combining loglinear models, we demonstrate how to apply ARM in a capture-recapture study and propose an approach to selecting a model list for combining given a high dimensional contingency table. In survival analysis, we empirically study combining different model classes. We also explore several measures to assess the predictive performance of a survival model. In the ANOVA setting, we propose model instability measures as a guide to the appropriateness of model combining in applications. We further systematically investigate the relationship between ARM performance and the underlying model structure. We propose an approach to assessing the importance of factors based on the combined estimates.;Finally, to address general computational issues, we have empirically explored the permutation times needed to produce stabilized weights for models and the relationship between ARM risk and the proportions used in the data splitting step of the algorithm. The results are largely consistent with our theoretical expectations
Kervolutional Neural Networks
Convolutional neural networks (CNNs) have enabled the state-of-the-art
performance in many computer vision tasks. However, little effort has been
devoted to establishing convolution in non-linear space. Existing works mainly
leverage on the activation layers, which can only provide point-wise
non-linearity. To solve this problem, a new operation, kervolution (kernel
convolution), is introduced to approximate complex behaviors of human
perception systems leveraging on the kernel trick. It generalizes convolution,
enhances the model capacity, and captures higher order interactions of
features, via patch-wise kernel functions, but without introducing additional
parameters. Extensive experiments show that kervolutional neural networks (KNN)
achieve higher accuracy and faster convergence than baseline CNN.Comment: oral paper in CVPR 201
Kernel Cross-Correlator
Cross-correlator plays a significant role in many visual perception tasks,
such as object detection and tracking. Beyond the linear cross-correlator, this
paper proposes a kernel cross-correlator (KCC) that breaks traditional
limitations. First, by introducing the kernel trick, the KCC extends the linear
cross-correlation to non-linear space, which is more robust to signal noises
and distortions. Second, the connection to the existing works shows that KCC
provides a unified solution for correlation filters. Third, KCC is applicable
to any kernel function and is not limited to circulant structure on training
data, thus it is able to predict affine transformations with customized
properties. Last, by leveraging the fast Fourier transform (FFT), KCC
eliminates direct calculation of kernel vectors, thus achieves better
performance yet still with a reasonable computational cost. Comprehensive
experiments on visual tracking and human activity recognition using wearable
devices demonstrate its robustness, flexibility, and efficiency. The source
codes of both experiments are released at https://github.com/wang-chen/KCCComment: The Thirty-Second AAAI Conference on Artificial Intelligence
(AAAI-18
Correlation Flow: Robust Optical Flow Using Kernel Cross-Correlators
Robust velocity and position estimation is crucial for autonomous robot
navigation. The optical flow based methods for autonomous navigation have been
receiving increasing attentions in tandem with the development of micro
unmanned aerial vehicles. This paper proposes a kernel cross-correlator (KCC)
based algorithm to determine optical flow using a monocular camera, which is
named as correlation flow (CF). Correlation flow is able to provide reliable
and accurate velocity estimation and is robust to motion blur. In addition, it
can also estimate the altitude velocity and yaw rate, which are not available
by traditional methods. Autonomous flight tests on a quadcopter show that
correlation flow can provide robust trajectory estimation with very low
processing power. The source codes are released based on the ROS framework.Comment: 2018 International Conference on Robotics and Automation (ICRA 2018
Non-iterative RGB-D-inertial Odometry
This paper presents a non-iterative solution to RGB-D-inertial odometry
system. Traditional odometry methods resort to iterative algorithms which are
usually computationally expensive or require well-designed initialization. To
overcome this problem, this paper proposes to combine a non-iterative front-end
(odometry) with an iterative back-end (loop closure) for the RGB-D-inertial
SLAM system. The main contribution lies in the novel non-iterative front-end,
which leverages on inertial fusion and kernel cross-correlators (KCC) to match
point clouds in frequency domain. Dominated by the fast Fourier transform
(FFT), our method is only of complexity , where is
the number of points. Map fusion is conducted by element-wise operations, so
that both time and space complexity are further reduced. Extensive experiments
show that, due to the lightweight of the proposed front-end, the framework is
able to run at a much faster speed yet still with comparable accuracy with the
state-of-the-arts
Graph Optimization Approach to Range-based Localization
In this paper, we propose a general graph optimization based framework for
localization, which can accommodate different types of measurements with
varying measurement time intervals. Special emphasis will be on range-based
localization. Range and trajectory smoothness constraints are constructed in a
position graph, then the robot trajectory over a sliding window is estimated by
a graph based optimization algorithm. Moreover, convergence analysis of the
algorithm is provided, and the effects of the number of iterations and window
size in the optimization on the localization accuracy are analyzed. Extensive
experiments on quadcopter under a variety of scenarios verify the effectiveness
of the proposed algorithm and demonstrate a much higher localization accuracy
than the existing range-based localization methods, especially in the altitude
direction
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