Abstract
This paper presents a method for estimating the six Degrees of Freedom (6DoF) pose of texture-less primitive-shaped objects from depth images. As the conventional methods for object pose estimation require rich texture or geometric features to the target objects, these methods are not suitable for texture-less and geometrically simple shaped objects. In order to estimate the pose of the primitive-shaped object, the parameters that represent primitive shapes are estimated. However, these methods explicitly limit the number of types of primitive shapes that can be estimated. We employ superquadrics as a primitive shape representation that can represent various types of primitive shapes with only a few parameters. In order to estimate the superquadric parameters of primitive-shaped objects, the point cloud of the object must be segmented from a depth image. It is known that the parameter estimation is sensitive to outliers, which are caused by the miss-segmentation of the depth image. Therefore, we propose a novel estimation method for superquadric parameters that are robust to outliers. In the experiment, we constructed a dataset in which the person grasps and moves the primitive-shaped objects. The experimental results show that our estimation method outperformed three conventional methods and the baseline method.
Highlights
The 3D pose estimation and tracking of objects plays an important role in object grasping by robots, scene understanding, augmented/virtual reality, and other applications
As our method enables us to estimate the pose of superquadrics even if the outlier points exist in the object point cloud, our method can handle a case in which a person freely moves objects
As our work is about primitive shape pose estimation using superquadric representation, we introduce the pose estimation of primitive shapes and research using superquadric representation
Summary
The 3D pose estimation and tracking of objects plays an important role in object grasping by robots, scene understanding, augmented/virtual reality, and other applications. The 3D objects can be tracked by estimating the sequential six Degrees of Freedom (6DoF) pose of the object. To estimate the object pose between successive frames, Iterative Closest Point (ICP) [7] is widely employed [8,9]. When the ICP algorithm is applied to the objects that have a limited number of geometrical features, the pose estimation is not accurate and unstable due to the difficulty in obtaining the correct corresponding keypoints. We aim to tackle the problem of pose estimation against geometrically simple (primitive-shaped), texture-less objects from sequential depth images. In this case, the above feature point-based methods and ICP pose estimation are unsuitable
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