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Your search keyword '"Orosco, Ryan"' showing total 7 results
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7 results on '"Orosco, Ryan"'

2. Image Based Reconstruction of Liquids from 2D Surface Detections

Author

Richter, Florian, Orosco, Ryan K., and Yip, Michael C.

Subjects
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, ComputingMethodologies_COMPUTERGRAPHICS
Abstract

In this work, we present a solution to the challenging problem of reconstructing liquids from image data. The challenges in reconstructing liquids, which is not faced in previous reconstruction works on rigid and deforming surfaces, lies in the inability to use depth sensing and color features due the variable index of refraction, opacity, and environmental reflections. Therefore, we limit ourselves to only surface detections (i.e. binary mask) of liquids as observations and do not assume any prior knowledge on the liquids properties. A novel optimization problem is posed which reconstructs the liquid as particles by minimizing the error between a rendered surface from the particles and the surface detections while satisfying liquid constraints. Our solvers to this optimization problem are presented and no training data is required to apply them. We also propose a dynamic prediction to seed the reconstruction optimization from the previous time-step. We test our proposed methods in simulation and on two new liquid datasets which we open source so the broader research community can continue developing in this under explored area., Comment: 14 pages, 11 figures, 2 tables

Published
2022

4. Robotic Tool Tracking Under Partially Visible Kinematic Chain: A Unified Approach.

Author

Richter, Florian, Lu, Jingpei, Orosco, Ryan K., and Yip, Michael C.

Subjects
KINEMATIC chains, ROBOTICS, ROBOT vision, ROBOT kinematics, CABLES
Abstract

Anytime a robot manipulator is controlled via visual feedback, the transformation between the robot and camera frame must be known. However, in the case where cameras can only capture a portion of the robot manipulator in order to better perceive the environment being interacted with, there is greater sensitivity to errors in calibration of the base-to-camera transform. A secondary source of uncertainty during robotic control are inaccuracies in joint angle measurements which can be caused by biases in positioning and complex transmission effects such as backlash and cable stretch. In this work, we bring together these two sets of unknown parameters into a unified problem formulation when the kinematic chain is partially visible in the camera view. We prove that these parameters are nonidentifiable implying that explicit estimation of them is infeasible. To overcome this, we derive a smaller set of parameters we call lumped error since it lumps together the errors of calibration and joint angle measurements. A particle filter method is presented and tested in simulation and on two real world robots to estimate the lumped error and show the efficiency of this parameter reduction. [ABSTRACT FROM AUTHOR]

Published
2022
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