2,193 research outputs found
MonoPerfCap: Human Performance Capture from Monocular Video
We present the first marker-less approach for temporally coherent 3D
performance capture of a human with general clothing from monocular video. Our
approach reconstructs articulated human skeleton motion as well as medium-scale
non-rigid surface deformations in general scenes. Human performance capture is
a challenging problem due to the large range of articulation, potentially fast
motion, and considerable non-rigid deformations, even from multi-view data.
Reconstruction from monocular video alone is drastically more challenging,
since strong occlusions and the inherent depth ambiguity lead to a highly
ill-posed reconstruction problem. We tackle these challenges by a novel
approach that employs sparse 2D and 3D human pose detections from a
convolutional neural network using a batch-based pose estimation strategy.
Joint recovery of per-batch motion allows to resolve the ambiguities of the
monocular reconstruction problem based on a low dimensional trajectory
subspace. In addition, we propose refinement of the surface geometry based on
fully automatically extracted silhouettes to enable medium-scale non-rigid
alignment. We demonstrate state-of-the-art performance capture results that
enable exciting applications such as video editing and free viewpoint video,
previously infeasible from monocular video. Our qualitative and quantitative
evaluation demonstrates that our approach significantly outperforms previous
monocular methods in terms of accuracy, robustness and scene complexity that
can be handled.Comment: Accepted to ACM TOG 2018, to be presented on SIGGRAPH 201
Pareto Meets Huber: Efficiently Avoiding Poor Minima in Robust Estimation
International audienceRobust cost optimization is the task of fitting parameters to data points containing outliers. In particular, we focus on large-scale computer vision problems, such as bundle adjustment , where Non-Linear Least Square (NLLS) solvers are the current workhorse. In this context, NLLS-based state of the art algorithms have been designed either to quickly improve the target objective and find a local minimum close to the initial value of the parameters, or to have a strong ability to avoid poor local minima. In this paper, we propose a novel algorithm relying on multi-objective optimization which allows to match those two properties. We experimentally demonstrate that our algorithm has an ability to avoid poor local minima that is on par with the best performing algorithms with a faster decrease of the target objective
Robust Legged Robot State Estimation Using Factor Graph Optimization
Legged robots, specifically quadrupeds, are becoming increasingly attractive
for industrial applications such as inspection. However, to leave the
laboratory and to become useful to an end user requires reliability in harsh
conditions. From the perspective of state estimation, it is essential to be
able to accurately estimate the robot's state despite challenges such as uneven
or slippery terrain, textureless and reflective scenes, as well as dynamic
camera occlusions. We are motivated to reduce the dependency on foot contact
classifications, which fail when slipping, and to reduce position drift during
dynamic motions such as trotting. To this end, we present a factor graph
optimization method for state estimation which tightly fuses and smooths
inertial navigation, leg odometry and visual odometry. The effectiveness of the
approach is demonstrated using the ANYmal quadruped robot navigating in a
realistic outdoor industrial environment. This experiment included trotting,
walking, crossing obstacles and ascending a staircase. The proposed approach
decreased the relative position error by up to 55% and absolute position error
by 76% compared to kinematic-inertial odometry.Comment: 8 pages, 12 figures. Accepted to RA-L + IROS 2019, July 201
On-Manifold Preintegration for Real-Time Visual-Inertial Odometry
Current approaches for visual-inertial odometry (VIO) are able to attain
highly accurate state estimation via nonlinear optimization. However, real-time
optimization quickly becomes infeasible as the trajectory grows over time, this
problem is further emphasized by the fact that inertial measurements come at
high rate, hence leading to fast growth of the number of variables in the
optimization. In this paper, we address this issue by preintegrating inertial
measurements between selected keyframes into single relative motion
constraints. Our first contribution is a \emph{preintegration theory} that
properly addresses the manifold structure of the rotation group. We formally
discuss the generative measurement model as well as the nature of the rotation
noise and derive the expression for the \emph{maximum a posteriori} state
estimator. Our theoretical development enables the computation of all necessary
Jacobians for the optimization and a-posteriori bias correction in analytic
form. The second contribution is to show that the preintegrated IMU model can
be seamlessly integrated into a visual-inertial pipeline under the unifying
framework of factor graphs. This enables the application of
incremental-smoothing algorithms and the use of a \emph{structureless} model
for visual measurements, which avoids optimizing over the 3D points, further
accelerating the computation. We perform an extensive evaluation of our
monocular \VIO pipeline on real and simulated datasets. The results confirm
that our modelling effort leads to accurate state estimation in real-time,
outperforming state-of-the-art approaches.Comment: 20 pages, 24 figures, accepted for publication in IEEE Transactions
on Robotics (TRO) 201
System calibration method for Fourier ptychographic microscopy
Fourier ptychographic microscopy (FPM) is a recently proposed quantitative
phase imaging technique with high resolution and wide field-of-view (FOV). In
current FPM imaging platforms, systematic error sources come from the
aberrations, LED intensity fluctuation, parameter imperfections and noise,
which will severely corrupt the reconstruction results with artifacts. Although
these problems have been researched and some special methods have been proposed
respectively, there is no method to solve all of them. However, the systematic
error is a mixture of various sources in the real situation. It is difficult to
distinguish a kind of error source from another due to the similar artifacts.
To this end, we report a system calibration procedure, termed SC-FPM, based on
the simulated annealing (SA) algorithm, LED intensity correction and adaptive
step-size strategy, which involves the evaluation of an error matric at each
iteration step, followed by the re-estimation of accurate parameters. The great
performance has been achieved both in simulation and experiments. The reported
system calibration scheme improves the robustness of FPM and relaxes the
experiment conditions, which makes the FPM more pragmatic.Comment: 18 pages, 9 figure
Choice of Interceptor Aerodynamic Lifting Surface Location based on Autopilot Design Considerations
Interceptors operate at wide range of operating conditions in terms of Mach number, altitude and angle of attack. The aerodynamic design caters for such wide operating envelope by appropriate sizing of lifting and control surfaces for meeting the normal acceleration capability requirements. The wide range of operating conditions leads to an inevitable spread in center of pressure location and hence spread in static stability. The performance of control design is a strong function of the aerodynamic static stability. The total operating envelope can be bifurcated into statically stable and unstable zones and the aerodynamic lifting surface location can be used as a control parameter to identify the neutral stability point. During the homing phase lesser static stability is desirable for good speed of response, hence the lifting surface location needs to be chosen based on the capability of control to handle instability. This paper analyses the limitations of autopilot design for the control of an unstable interceptor and brings out a method to identify the optimum aerodynamic lifting surface location for efficiently managing static margin while satisfying the control limitations and homing phase performance. This provides an input on the most appropriate lifting surface location to the aerodynamic designer during the initial CFD based aerodynamic characterisation stage itself, before commencing the rigorous wind tunnel based characterisation
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