285,640 research outputs found
Learning to Predict Dense Correspondences for 6D Pose Estimation
Object pose estimation is an important problem in computer vision with applications in robotics, augmented reality and many other areas. An established strategy for object pose estimation consists of, firstly, finding correspondences between the image and the object’s reference frame, and, secondly, estimating the pose from outlier-free correspondences using Random Sample Consensus (RANSAC). The first step, namely finding correspondences, is difficult because object appearance varies depending on perspective, lighting and many other factors. Traditionally, correspondences have been established using handcrafted methods like sparse feature pipelines.
In this thesis, we introduce a dense correspondence representation for objects, called object coordinates, which can be learned. By learning object coordinates, our pose estimation pipeline adapts to various aspects of the task at hand. It works well for diverse object types, from small objects to entire rooms, varying object attributes, like textured or texture-less objects, and different input modalities, like RGB-D or RGB images. The concept of object coordinates allows us to easily model and exploit uncertainty as part of the pipeline such that even repeating structures or areas with little texture can contribute to a good solution. Although we can train object coordinate predictors independent of the full pipeline and achieve good results, training the pipeline in an end-to-end fashion is desirable. It enables the object coordinate predictor to adapt its output to the specificities of following steps in the pose estimation pipeline. Unfortunately, the RANSAC component of the pipeline is non-differentiable which prohibits end-to-end training. Adopting techniques from reinforcement learning, we introduce Differentiable Sample Consensus (DSAC), a formulation of RANSAC which allows us to train the pose estimation pipeline in an end-to-end fashion by minimizing the expectation of the final pose error
Local sensory control of a dexterous end effector
A numerical scheme was developed to solve the inverse kinematics for a user-defined manipulator. The scheme was based on a nonlinear least-squares technique which determines the joint variables by minimizing the difference between the target end effector pose and the actual end effector pose. The scheme was adapted to a dexterous hand in which the joints are either prismatic or revolute and the fingers are considered open kinematic chains. Feasible solutions were obtained using a three-fingered dexterous hand. An algorithm to estimate the position and orientation of a pre-grasped object was also developed. The algorithm was based on triangulation using an ideal sensor and a spherical object model. By choosing the object to be a sphere, only the position of the object frame was important. Based on these simplifications, a minimum of three sensors are needed to find the position of a sphere. A two dimensional example to determine the position of a circle coordinate frame using a two-fingered dexterous hand was presented
Data-Driven Grasp Synthesis - A Survey
We review the work on data-driven grasp synthesis and the methodologies for
sampling and ranking candidate grasps. We divide the approaches into three
groups based on whether they synthesize grasps for known, familiar or unknown
objects. This structure allows us to identify common object representations and
perceptual processes that facilitate the employed data-driven grasp synthesis
technique. In the case of known objects, we concentrate on the approaches that
are based on object recognition and pose estimation. In the case of familiar
objects, the techniques use some form of a similarity matching to a set of
previously encountered objects. Finally for the approaches dealing with unknown
objects, the core part is the extraction of specific features that are
indicative of good grasps. Our survey provides an overview of the different
methodologies and discusses open problems in the area of robot grasping. We
also draw a parallel to the classical approaches that rely on analytic
formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic
Intuitive Hand Teleoperation by Novice Operators Using a Continuous Teleoperation Subspace
Human-in-the-loop manipulation is useful in when autonomous grasping is not
able to deal sufficiently well with corner cases or cannot operate fast enough.
Using the teleoperator's hand as an input device can provide an intuitive
control method but requires mapping between pose spaces which may not be
similar. We propose a low-dimensional and continuous teleoperation subspace
which can be used as an intermediary for mapping between different hand pose
spaces. We present an algorithm to project between pose space and teleoperation
subspace. We use a non-anthropomorphic robot to experimentally prove that it is
possible for teleoperation subspaces to effectively and intuitively enable
teleoperation. In experiments, novice users completed pick and place tasks
significantly faster using teleoperation subspace mapping than they did using
state of the art teleoperation methods.Comment: ICRA 2018, 7 pages, 7 figures, 2 table
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