3 research outputs found
Object Sorting Using a Global Texture-Shape 3D Feature Descriptor
Object recognition and grasping plays a key role in robotic systems,
especially for the autonomous robots to implement object sorting tasks in a
warehouse. In this paper, we present a global texture-shape 3D feature
descriptor which can be utilized in a system of object recognition and
grasping, and can perform object sorting tasks well. Our proposed descriptor
stems from the clustered viewpoint feature histogram (CVFH), which relies on
the geometrical information of the whole 3D object surface only, and can not
perform well in recognizing the objects with similar geometrical information.
Therefore, we extend the CVFH descriptor with texture and color information to
generate a new global 3D feature descriptor. The proposed descriptor is
evaluated in tasks of recognizing and classifying 3D objects by applying
multi-class support vector machines (SVM) in both public 3D image dataset and
real scenes. The results of evaluation show that the proposed descriptor
achieves a significant better performance for object recognition compared with
the original CVFH. Then, the proposed descriptor is applied in our object
recognition and grasping system, showing that the proposed descriptor helps the
system implement the object recognition, object grasping and object sorting
tasks well
Hierarchical object geometric categorization and appearance classification for mobile manipulation
Abstract — In this paper we present a comprehensive object categorization and classification system, of great importance for mobile manipulation applications in indoor environments. In detail, we tackle the problem of recognizing everyday objects that are useful for a personal robotic assistant in fulfilling its tasks, using a hierarchical multi-modal 3D-2D processing and classification system. The acquired 3D data is used to estimate geometric labels (plane, cylinder, edge, rim, sphere) at each voxel cell using the Radius-based Surface Descriptor (RSD). Then, we propose the use of a Global RSD feature (GRSD) to categorize point clusters that are geometrically identical into one of the object categories. Once a geometric category and a 3D position is obtained for each object cluster, we extract the region of interest in the camera image and compute a SURF-based feature vector for it. Thus we obtain the exact object instanc
Interactive Open-Ended Learning for 3D Object Recognition
The thesis contributes in several important ways to the research area of 3D
object category learning and recognition. To cope with the mentioned
limitations, we look at human cognition, in particular at the fact that human
beings learn to recognize object categories ceaselessly over time. This ability
to refine knowledge from the set of accumulated experiences facilitates the
adaptation to new environments. Inspired by this capability, we seek to create
a cognitive object perception and perceptual learning architecture that can
learn 3D object categories in an open-ended fashion. In this context,
``open-ended'' implies that the set of categories to be learned is not known in
advance, and the training instances are extracted from actual experiences of a
robot, and thus become gradually available, rather than being available since
the beginning of the learning process. In particular, this architecture
provides perception capabilities that will allow robots to incrementally learn
object categories from the set of accumulated experiences and reason about how
to perform complex tasks. This framework integrates detection, tracking,
teaching, learning, and recognition of objects. An extensive set of systematic
experiments, in multiple experimental settings, was carried out to thoroughly
evaluate the described learning approaches. Experimental results show that the
proposed system is able to interact with human users, learn new object
categories over time, as well as perform complex tasks. The contributions
presented in this thesis have been fully implemented and evaluated on different
standard object and scene datasets and empirically evaluated on different
robotic platforms.Comment: PhD thesi