Vision-based Robotic Grasping from Object Localization, Pose Estimation, Grasp Detection to Motion Planning: A Review <s> Voting-based methods <s> Object detection and localization is a crucial step for inspection and manipulation tasks in robotic and industrial applications. We present an object detection and localization scheme for 3D objects that combines intensity and depth data. A novel multimodal, scale- and rotation-invariant feature is used to simultaneously describe the object's silhouette and surface appearance. The object's position is determined by matching scene and model features via a Hough-like local voting scheme. The proposed method is quantitatively and qualitatively evaluated on a large number of real sequences, proving that it is generic and highly robust to occlusions and clutter. Comparisons with state of the art methods demonstrate comparable results and higher robustness with respect to occlusions. <s> BIB001 </s> Vision-based Robotic Grasping from Object Localization, Pose Estimation, Grasp Detection to Motion Planning: A Review <s> Voting-based methods <s> This work addresses the problem of estimating the 6D Pose of specific objects from a single RGB-D image. We present a flexible approach that can deal with generic objects, both textured and texture-less. The key new concept is a learned, intermediate representation in form of a dense 3D object coordinate labelling paired with a dense class labelling. We are able to show that for a common dataset with texture-less objects, where template-based techniques are suitable and state of the art, our approach is slightly superior in terms of accuracy. We also demonstrate the benefits of our approach, compared to template-based techniques, in terms of robustness with respect to varying lighting conditions. Towards this end, we contribute a new ground truth dataset with 10k images of 20 objects captured each under three different lighting conditions. We demonstrate that our approach scales well with the number of objects and has capabilities to run fast. <s> BIB002 </s> Vision-based Robotic Grasping from Object Localization, Pose Estimation, Grasp Detection to Motion Planning: A Review <s> Voting-based methods <s> In this paper we propose a novel framework, Latent-Class Hough Forests, for 3D object detection and pose estimation in heavily cluttered and occluded scenes. Firstly, we adapt the state-of-the-art template matching feature, LINEMOD [14], into a scale-invariant patch descriptor and integrate it into a regression forest using a novel template-based split function. In training, rather than explicitly collecting representative negative samples, our method is trained on positive samples only and we treat the class distributions at the leaf nodes as latent variables. During the inference process we iteratively update these distributions, providing accurate estimation of background clutter and foreground occlusions and thus a better detection rate. Furthermore, as a by-product, the latent class distributions can provide accurate occlusion aware segmentation masks, even in the multi-instance scenario. In addition to an existing public dataset, which contains only single-instance sequences with large amounts of clutter, we have collected a new, more challenging, dataset for multiple-instance detection containing heavy 2D and 3D clutter as well as foreground occlusions. We evaluate the Latent-Class Hough Forest on both of these datasets where we outperform state-of-the art methods. <s> BIB003 </s> Vision-based Robotic Grasping from Object Localization, Pose Estimation, Grasp Detection to Motion Planning: A Review <s> Voting-based methods <s> The Point Pair Feature [4] has been one of the most successful 6D pose estimation method among model-based approaches as an efficient, integrated and compromise alternative to the traditional local and global pipelines. During the last years, several variations of the algorithm have been proposed. Among these extensions, the solution introduced by Hinterstoisser et al. [6] is a major contribution. This work presents a variation of this PPF method applied to the SIXD Challenge datasets presented at the 3rd International Workshop on Recovering 6D Object Pose held at the ICCV 2017. We report an average recall of 0.77 for all datasets and overall recall of 0.82, 0.67, 0.85, 0.37, 0.97 and 0.96 for hinterstoisser, tless, tudlight, rutgers, tejani and doumanoglou datasets, respectively. <s> BIB004
Typical functional flow-chart of voting-based object pose estimation methods without object detection is illustrated in Fig. 17 . This kind of methods are mainly used for computing the poses of objects with occlusions. For these objects, the local evidence in the image restricts the possible outcome of the desired output, and every image patch is thus usually used to cast a vote about the output. Brachmann et al. BIB002 proposed a learned, intermediate representation in form of a dense 3D object coordinate labelling paired with a dense class labelling. Each object coordinate prediction defines a 3D-3D correspondence between the image and the 3D object model, and the pose hypotheses are generated and refined to obtain the final hypothesis. Tejani et al. BIB003 trained a Hough forest for 6D pose estimation from an RGB-D image. Each tree in the forest maps an image patch to a leaf which stores a set of 6D pose votes. Drost et al. BIB001 proposed the Point Pair Features(PPF) to recover a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image, and a variation of PPF BIB004 won the SIXD challenge. Wang et al.  proposed a generic framework named DenseFusion for estimating the 6D pose of a set of known objects from RGB-D images. DenseFusion is a heterogeneous architecture that processes the two data sources (RGB and depth) independently and uses a novel network to extract pixel-wise dense feature embeddings, from which the pose is estimated. The predictions are voted to generate the final 6D pose prediction of the object.