109,335 research outputs found
Simultaneous Hand Pose and Skeleton Bone-Lengths Estimation from a Single Depth Image
Articulated hand pose estimation is a challenging task for human-computer
interaction. The state-of-the-art hand pose estimation algorithms work only
with one or a few subjects for which they have been calibrated or trained.
Particularly, the hybrid methods based on learning followed by model fitting or
model based deep learning do not explicitly consider varying hand shapes and
sizes. In this work, we introduce a novel hybrid algorithm for estimating the
3D hand pose as well as bone-lengths of the hand skeleton at the same time,
from a single depth image. The proposed CNN architecture learns hand pose
parameters and scale parameters associated with the bone-lengths
simultaneously. Subsequently, a new hybrid forward kinematics layer employs
both parameters to estimate 3D joint positions of the hand. For end-to-end
training, we combine three public datasets NYU, ICVL and MSRA-2015 in one
unified format to achieve large variation in hand shapes and sizes. Among
hybrid methods, our method shows improved accuracy over the state-of-the-art on
the combined dataset and the ICVL dataset that contain multiple subjects. Also,
our algorithm is demonstrated to work well with unseen images.Comment: This paper has been accepted and presented in 3DV-2017 conference
held at Qingdao, China. http://irc.cs.sdu.edu.cn/3dv
Realtime State Estimation with Tactile and Visual sensing. Application to Planar Manipulation
Accurate and robust object state estimation enables successful object
manipulation. Visual sensing is widely used to estimate object poses. However,
in a cluttered scene or in a tight workspace, the robot's end-effector often
occludes the object from the visual sensor. The robot then loses visual
feedback and must fall back on open-loop execution.
In this paper, we integrate both tactile and visual input using a framework
for solving the SLAM problem, incremental smoothing and mapping (iSAM), to
provide a fast and flexible solution. Visual sensing provides global pose
information but is noisy in general, whereas contact sensing is local, but its
measurements are more accurate relative to the end-effector. By combining them,
we aim to exploit their advantages and overcome their limitations. We explore
the technique in the context of a pusher-slider system. We adapt iSAM's
measurement cost and motion cost to the pushing scenario, and use an
instrumented setup to evaluate the estimation quality with different object
shapes, on different surface materials, and under different contact modes
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