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.
The local 3D feature descriptor relies on the geometrical information of the 3D object surface. Each local 3D feature descriptor is computed for a single key point of the 3D object surface, thus each 3D object have more than one local 3D feature descriptor. A set of all local 3D feature descriptors can be used for object recognition by means of feature matching. Several local 3D feature descriptors are proposed for object recognition, like the 3D shape context (3DSC) @cite_11 , the unique shape context descriptor (USC) @cite_17 , the normal aligned radial feature (NARF) @cite_19 and the rotational projection statistics (RoPS) @cite_12 . These descriptors can obtain a good performance on the objects which contain rich geometrical information for object recognition. In addition, They don't need to undergo the stage of segmentation when they are applied on object recognition. But these local 3D feature descriptors need a lot of computational resource for computing descriptors and descriptor matching when they are used for object recognition.
@cite_19 @cite_17 @cite_12 @cite_11
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3D object recognition from local features is robust to occlusions and clutter. However, local features must be extracted from a small set of feature rich keypoints to avoid computational complexity and ambiguous features. We present an algorithm for the detection of such keypoints on 3D models and partial views of objects. The keypoints are highly repeatable between partial views of an object and its complete 3D model. We also propose a quality measure to rank the keypoints and select the best ones for extracting local features. Keypoints are identified at locations where a unique local 3D coordinate basis can be derived from the underlying surface in order to extract invariant features. We also propose an automatic scale selection technique for extracting multi-scale and scale invariant features to match objects at different unknown scales. Features are projected to a PCA subspace and matched to find correspondences between a database and query object. Each pair of matching features gives a transformation that aligns the query and database object. These transformations are clustered and the biggest cluster is used to identify the query object. Experiments on a public database revealed that the proposed quality measure relates correctly to the repeatability of keypoints and the multi-scale features have a recognition rate of over 95 for up to 80 occluded objects. Motivated by the discriminative ability of shape information and local patterns in object recognition, this paper proposes a window-based object descriptor that integrates both cues. In particular, contour templates representing object shape are used to derive a set of so-called key points at which local appearance features are extracted. These key points are located using an improved template matching method that utilises both spatial and orientation information in a simple and effective way. At each of the extracted key points, a new local appearance feature, namely non-redundant local binary pattern (NR-LBP), is computed. An object descriptor is formed by concatenating the NR-LBP features from all key points to encode the shape as well as the appearance of the object. The proposed descriptor was extensively tested in the task of detecting humans from static images on the commonly used MIT and INRIA datasets. The experimental results have shown that the proposed descriptor can effectively describe non-rigid objects with high articulation and improve the detection rate compared to other state-of-the-art object descriptors. 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 instance and the orientation around the object's up-right axis from the appearance. The resultant system provides a hierarchical categorization of objects into basic classes from their geometry and identifies objects and their poses based on their appearance, with near real-time performance. We validate our approach on an extensive database of objects that we acquired using real sensing devices, and on both unseen views and unseen objects. 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