49,163 research outputs found
A Robust Model Predictive Control Approach for Autonomous Underwater Vehicles Operating in a Constrained workspace
This paper presents a novel Nonlinear Model Predictive Control (NMPC) scheme
for underwater robotic vehicles operating in a constrained workspace including
static obstacles. The purpose of the controller is to guide the vehicle towards
specific way points. Various limitations such as: obstacles, workspace
boundary, thruster saturation and predefined desired upper bound of the vehicle
velocity are captured as state and input constraints and are guaranteed during
the control design. The proposed scheme incorporates the full dynamics of the
vehicle in which the ocean currents are also involved. Hence, the control
inputs calculated by the proposed scheme are formulated in a way that the
vehicle will exploit the ocean currents, when these are in favor of the
way-point tracking mission which results in reduced energy consumption by the
thrusters. The performance of the proposed control strategy is experimentally
verified using a Degrees of Freedom (DoF) underwater robotic vehicle inside
a constrained test tank with obstacles.Comment: IEEE International Conference on Robotics and Automation (ICRA-2018),
Accepte
Pop-up SLAM: Semantic Monocular Plane SLAM for Low-texture Environments
Existing simultaneous localization and mapping (SLAM) algorithms are not
robust in challenging low-texture environments because there are only few
salient features. The resulting sparse or semi-dense map also conveys little
information for motion planning. Though some work utilize plane or scene layout
for dense map regularization, they require decent state estimation from other
sources. In this paper, we propose real-time monocular plane SLAM to
demonstrate that scene understanding could improve both state estimation and
dense mapping especially in low-texture environments. The plane measurements
come from a pop-up 3D plane model applied to each single image. We also combine
planes with point based SLAM to improve robustness. On a public TUM dataset,
our algorithm generates a dense semantic 3D model with pixel depth error of 6.2
cm while existing SLAM algorithms fail. On a 60 m long dataset with loops, our
method creates a much better 3D model with state estimation error of 0.67%.Comment: International Conference on Intelligent Robots and Systems (IROS)
201
Adaptive User Perspective Rendering for Handheld Augmented Reality
Handheld Augmented Reality commonly implements some variant of magic lens
rendering, which turns only a fraction of the user's real environment into AR
while the rest of the environment remains unaffected. Since handheld AR devices
are commonly equipped with video see-through capabilities, AR magic lens
applications often suffer from spatial distortions, because the AR environment
is presented from the perspective of the camera of the mobile device. Recent
approaches counteract this distortion based on estimations of the user's head
position, rendering the scene from the user's perspective. To this end,
approaches usually apply face-tracking algorithms on the front camera of the
mobile device. However, this demands high computational resources and therefore
commonly affects the performance of the application beyond the already high
computational load of AR applications. In this paper, we present a method to
reduce the computational demands for user perspective rendering by applying
lightweight optical flow tracking and an estimation of the user's motion before
head tracking is started. We demonstrate the suitability of our approach for
computationally limited mobile devices and we compare it to device perspective
rendering, to head tracked user perspective rendering, as well as to fixed
point of view user perspective rendering
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