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.
These 3D feature descriptors rely on the geometrical information of the whole 3D object surface only. Thus these descriptors can obtain a good performance on the object with different shapes, but they cannot perform well on the objects with similar shapes. Some 3D feature descriptors are also based on both shape and texture information, like the Color-SHOT (CSHOT) @cite_16 . It is a local 3D feature descriptor which combine the shape information with the color information. Obviously, when this local 3D feature descriptor is used for implementing object recognition, it requires computational resources for computing descriptors and descriptor matching, which worsen the situation of computing burden.
@cite_16
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Motivated by the increasing availability of 3D sensors capable of delivering both shape and texture information, this paper presents a novel descriptor for feature matching in 3D data enriched with texture. The proposed approach stems from the theory of a recently proposed descriptor for 3D data which relies on shape only, and represents its generalization to the case of multiple cues associated with a 3D mesh. The proposed descriptor, dubbed CSHOT, is demonstrated to notably improve the accuracy of feature matching in challenging object recognition scenarios characterized by the presence of clutter and occlusions. AbstractThis paper presents alocal3D descriptor forsurface matching dubbed SHOT.Our proposal stems from a taxonomy of existing methods which highlightstwo major approaches, referred to as Signatures and Histograms, inher-ently emphasizing descriptiveness and robustness respectively. We formulatea comprehensive proposal which encompasses a repeatable local referenceframe as well as a 3D descriptor, the latter featuring an hybrid structurebetween Signatures and Histograms so as to aim at a more favorable bal-ance between descriptive power and robustness. A quite peculiar trait of ourmethod concerns seamless integration of multiple cues within the descrip-tor to improve distinctiveness, which is particularly relevant nowadays dueto the increasing availability of aﬀordable RGB-D sensors which can gatherboth depth and color information. A thorough experimental evaluation basedon datasets acquired with diﬀerent types of sensors, including a novel RGB-D dataset, vouches that SHOT outperforms state-of-the-art local descriptorsin experiments addressing descriptor matching for object recognition, 3Dreconstruction and shape retrieval.Keywords: Surface Matching, 3D Descriptors, Object Recognition, 3DReconstruction1. IntroductionAutomatic recognition of shapes in 3D data, also referred to as surfacematching, attracts growing interest in the research community, with appli-cation to areas such as shape retrieval, 3D reconstruction, object recogni-tion categorization, manipulation and grasping, robot localization and nav-igation. A key enabling factor for the development of this technology is Often practitioners face the issue of choosing the most adequate 3D features for their problem. As with many other problems, there isn’t a “best” feature to use. Nonetheless, we can gauge the relative accuracy of available features on common datasets. That is what we propose to do in this paper: we describe the existing feature extraction algorithms in a public available point cloud library and perform a comparative evaluation on 3D point clouds, investigation both the object and category recognition performance. The main conclusions are: increasing the number of keypoints improves recognition results at the expense of size and time; since there are big differences in terms of recognition performance, size and time requirements, the descriptor has to be matched to the desired task; one should use a descriptor that takes advantage of color; the SHOTCOLOR descriptor shows a good balance between recognition accuracy and time complexity. Detecting poorly textured objects and estimating their 3D pose reliably is still a very challenging problem. We introduce a simple but powerful approach to computing descriptors for object views that efficiently capture both the object identity and 3D pose. By contrast with previous manifold-based approaches, we can rely on the Euclidean distance to evaluate the similarity between descriptors, and therefore use scalable Nearest Neighbor search methods to efficiently handle a large number of objects under a large range of poses. To achieve this, we train a Convolutional Neural Network to compute these descriptors by enforcing simple similarity and dissimilarity constraints between the descriptors. We show that our constraints nicely untangle the images from different objects and different views into clusters that are not only well-separated but also structured as the corresponding sets of poses: The Euclidean distance between descriptors is large when the descriptors are from different objects, and directly related to the distance between the poses when the descriptors are from the same object. These important properties allow us to outperform state-of-the-art object views representations on challenging RGB and RGB-D data.