845 research outputs found
Parameter-unaware autocalibration for occupancy mapping
People localization and occupancy mapping are common and important tasks for multi-camera systems. In this paper, we present a novel approach to overcome the hurdle of manual extrinsic calibration of the multi-camera system. Our approach is completely parameter unaware, meaning that the user does not need to know the focal length, position or viewing angle in advance, nor will these values be calibrated as such. The only requirement to the multi-camera setup is that the views overlap substantially and are mounted at approximately the same height, requirements that are satisfied in most typical multi-camera configurations. The proposed method uses the observed height of an object or person moving through the space to estimate the distance to the object or person. Using this distance to backproject the lowest point of each detected object, we obtain a rotated and anisotropically scaled view of the ground plane for each camera. An algorithm is presented to estimate the anisotropic scaling parameters and rotation for each camera, after which ground plane positions can be computed up to an isotropic scale factor. Lens distortion is not taken into account. The method is tested in simulation yielding average accuracies within 5cm, and in a real multi-camera environment with an accuracy within 15cm
A Model for Optimal Human Navigation with Stochastic Effects
We present a method for optimal path planning of human walking paths in
mountainous terrain, using a control theoretic formulation and a
Hamilton-Jacobi-Bellman equation. Previous models for human navigation were
entirely deterministic, assuming perfect knowledge of the ambient elevation
data and human walking velocity as a function of local slope of the terrain.
Our model includes a stochastic component which can account for uncertainty in
the problem, and thus includes a Hamilton-Jacobi-Bellman equation with
viscosity. We discuss the model in the presence and absence of stochastic
effects, and suggest numerical methods for simulating the model. We discuss two
different notions of an optimal path when there is uncertainty in the problem.
Finally, we compare the optimal paths suggested by the model at different
levels of uncertainty, and observe that as the size of the uncertainty tends to
zero (and thus the viscosity in the equation tends to zero), the optimal path
tends toward the deterministic optimal path
L\'evy walks
Random walk is a fundamental concept with applications ranging from quantum
physics to econometrics. Remarkably, one specific model of random walks appears
to be ubiquitous across many fields as a tool to analyze transport phenomena in
which the dispersal process is faster than dictated by Brownian diffusion. The
L\'{e}vy walk model combines two key features, the ability to generate
anomalously fast diffusion and a finite velocity of a random walker. Recent
results in optics, Hamiltonian chaos, cold atom dynamics, bio-physics, and
behavioral science demonstrate that this particular type of random walks
provides significant insight into complex transport phenomena. This review
provides a self-consistent introduction to L\'{e}vy walks, surveys their
existing applications, including latest advances, and outlines further
perspectives.Comment: 50 page
Propagation Kernels
We introduce propagation kernels, a general graph-kernel framework for
efficiently measuring the similarity of structured data. Propagation kernels
are based on monitoring how information spreads through a set of given graphs.
They leverage early-stage distributions from propagation schemes such as random
walks to capture structural information encoded in node labels, attributes, and
edge information. This has two benefits. First, off-the-shelf propagation
schemes can be used to naturally construct kernels for many graph types,
including labeled, partially labeled, unlabeled, directed, and attributed
graphs. Second, by leveraging existing efficient and informative propagation
schemes, propagation kernels can be considerably faster than state-of-the-art
approaches without sacrificing predictive performance. We will also show that
if the graphs at hand have a regular structure, for instance when modeling
image or video data, one can exploit this regularity to scale the kernel
computation to large databases of graphs with thousands of nodes. We support
our contributions by exhaustive experiments on a number of real-world graphs
from a variety of application domains
Evolution strategies combined with central pattern generators for head motion minimization during quadruped robot locomotion
In autonomous robotics, the head shaking induced
by locomotion is a relevant and still not solved problem. This
problem constraints stable image acquisition and the possibility
to rely on that information to act accordingly.
In this article, we propose a movement controller to generate
locomotion and head movement. Our aim is to generate the
head movement required to minimize the head motion induced
by locomotion itself. The movement controllers are biologically
inspired in the concept of Central Pattern Generators (CPGs).
CPGs are modelled based on nonlinear dynamical systems,
coupled Hopf oscillators. This approach allows to explicitly
specify parameters such as amplitude, offset and frequency of
movement and to smoothly modulate the generated oscillations
according to changes in these parameters. Based on these
ideas, we propose a combined approach to generate head
movement stabilization on a quadruped robot, using CPGs and
an evolution strategy. The best set of parameters that generates
the head movement are computed by an evolution strategy.
Experiments were performed on a simulated AIBO robot.
The obtained results demonstrate the feasibility of the approach,
by reducing the overall head movement
Out of Distribution Detection via Domain-Informed Gaussian Process State Space Models
In order for robots to safely navigate in unseen scenarios using
learning-based methods, it is important to accurately detect
out-of-training-distribution (OoD) situations online. Recently, Gaussian
process state-space models (GPSSMs) have proven useful to discriminate
unexpected observations by comparing them against probabilistic predictions.
However, the capability for the model to correctly distinguish between in- and
out-of-training distribution observations hinges on the accuracy of these
predictions, primarily affected by the class of functions the GPSSM kernel can
represent. In this paper, we propose (i) a novel approach to embed existing
domain knowledge in the kernel and (ii) an OoD online runtime monitor, based on
receding-horizon predictions. Domain knowledge is provided in the form of a
dataset, collected either in simulation or by using a nominal model. Numerical
results show that the informed kernel yields better regression quality with
smaller datasets, as compared to standard kernel choices. We demonstrate the
effectiveness of the OoD monitor on a real quadruped navigating an indoor
setting, which reliably classifies previously unseen terrains.Comment: 7 pages, 4 figure
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