969 research outputs found
Ambiguity-Aware Multi-Object Pose Optimization for Visually-Assisted Robot Manipulation
6D object pose estimation aims to infer the relative pose between the object
and the camera using a single image or multiple images. Most works have focused
on predicting the object pose without associated uncertainty under occlusion
and structural ambiguity (symmetricity). However, these works demand prior
information about shape attributes, and this condition is hardly satisfied in
reality; even asymmetric objects may be symmetric under the viewpoint change.
In addition, acquiring and fusing diverse sensor data is challenging when
extending them to robotics applications. Tackling these limitations, we present
an ambiguity-aware 6D object pose estimation network, PrimA6D++, as a generic
uncertainty prediction method. The major challenges in pose estimation, such as
occlusion and symmetry, can be handled in a generic manner based on the
measured ambiguity of the prediction. Specifically, we devise a network to
reconstruct the three rotation axis primitive images of a target object and
predict the underlying uncertainty along each primitive axis. Leveraging the
estimated uncertainty, we then optimize multi-object poses using visual
measurements and camera poses by treating it as an object SLAM problem. The
proposed method shows a significant performance improvement in T-LESS and
YCB-Video datasets. We further demonstrate real-time scene recognition
capability for visually-assisted robot manipulation. Our code and supplementary
materials are available at https://github.com/rpmsnu/PrimA6D.Comment: IEEE Robotics and Automation Letter
Learning Implicit Probability Distribution Functions for Symmetric Orientation Estimation from RGB Images Without Pose Labels
Object pose estimation is a necessary prerequisite for autonomous robotic
manipulation, but the presence of symmetry increases the complexity of the pose
estimation task. Existing methods for object pose estimation output a single 6D
pose. Thus, they lack the ability to reason about symmetries. Lately, modeling
object orientation as a non-parametric probability distribution on the SO(3)
manifold by neural networks has shown impressive results. However, acquiring
large-scale datasets to train pose estimation models remains a bottleneck. To
address this limitation, we introduce an automatic pose labeling scheme. Given
RGB-D images without object pose annotations and 3D object models, we design a
two-stage pipeline consisting of point cloud registration and
render-and-compare validation to generate multiple symmetrical
pseudo-ground-truth pose labels for each image. Using the generated pose
labels, we train an ImplicitPDF model to estimate the likelihood of an
orientation hypothesis given an RGB image. An efficient hierarchical sampling
of the SO(3) manifold enables tractable generation of the complete set of
symmetries at multiple resolutions. During inference, the most likely
orientation of the target object is estimated using gradient ascent. We
evaluate the proposed automatic pose labeling scheme and the ImplicitPDF model
on a photorealistic dataset and the T-Less dataset, demonstrating the
advantages of the proposed method
Robust 6D Object Pose Estimation by Learning RGB-D Features
Accurate 6D object pose estimation is fundamental to robotic manipulation and
grasping. Previous methods follow a local optimization approach which minimizes
the distance between closest point pairs to handle the rotation ambiguity of
symmetric objects. In this work, we propose a novel discrete-continuous
formulation for rotation regression to resolve this local-optimum problem. We
uniformly sample rotation anchors in SO(3), and predict a constrained deviation
from each anchor to the target, as well as uncertainty scores for selecting the
best prediction. Additionally, the object location is detected by aggregating
point-wise vectors pointing to the 3D center. Experiments on two benchmarks:
LINEMOD and YCB-Video, show that the proposed method outperforms
state-of-the-art approaches. Our code is available at
https://github.com/mentian/object-posenet.Comment: Accepted at ICRA 202
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